*2.3. Spectral Data Acquisition*

Spectral data acquisition was performed both on whole cocoa beans and on de-husked cocoa bean powder. About 100 g of randomly chosen whole cocoa beans from each sample were scanned with a portable instrument (PoliSPEC-NIR, ITPhotonics, Breganze, Italy) and with the benchtop instrument (FOSS DS−2500 scanning monochromator FossNIR-System, Hillerød, Denmark). Both NIR data acquisitions were performed in reflectance mode, with the following parameters:

#### *2.4. Chemical Analyses*

Unless otherwise specified, analyses were performed according to official methods of analysis (AOAC, 2016). All chemical analyses were performed in triplicate on peeled and ground cocoa beans.

#### 2.4.1. Dry Matter

Dry matter is measured as subtraction of the moisture content measured using a gravimetric method based on AOAC method 931.04 [21]. Hereto, approximately 2 g of powder sample were dried at 101–103 ◦C to constant weight in a forced-air electric oven (UF55 Plus, Memmert, Schwabach, Germany). After the drying process was completed, the samples were immediately closed with glass lids to avoid exposure and stored in desiccators for one hour to equilibrate samples towards ambient temperature [27]. The moisture content was expressed as average percentage (%) based on loss in weight of three independent samples.

#### 2.4.2. Ash

For the measurement of ashes, the sample was charred on a plate and placed in a muffle furnace (Gefran Model 1200; Gefran Spa, Brescia, Italy) at 550 ◦C (AOAC 972.15A). Ash content was expressed as weight percentage (%).

#### 2.4.3.  Fat content was expressed as weight percentage (%).

#### 2.4.4. Total Protein Content

Protein determination was carried out by the Kjeldahl method, as described in AOAC 2016 (method 970.22) (model Kjeltec 2300-Foss Analytical). The protein content was calculated from the concentration of total nitrogen by applying a conversion factor of 6.25.

#### 2.4.5. Total Phenolic Content

The total phenolic content was determined according to the colorimetric method of Folin–Ciocalteu [28]. Samples were defatted using the Soxhlet method (AOAC 963.15). Defatted powder (0.05 g) was added to 10 mL of a methanol-water (70:30 *v*/*v*) mixture at room temperature and stirred for 45 min. After centrifugation, 0.1 mL of solution was mixed with 3 mL of distilled water and 0.5 mL Folin–Ciocalteu reagent. The mixture was stored for 3 min after which 1 mL of aqueous Na2CO3 (200 g L−1) was added. The mixture was allowed to stand for 20 min at 40 ◦C and the total polyphenols were determined by spectrophotometry at 765 nm (spectrophotometer model Cary 60 UV-Vis Agilent Technologies Stevens Creek Blvd. Santa Clara, CA, USA). The standard curve was prepared using 0, 50, 100, 150, 200 and 250 mg L−1 solutions of gallic acid in methanol. Total phenol values were expressed in terms of gallic acid equivalents (mg g−1 of dry fat-free mass) [29]. The analyses were performed in triplicate.

#### 2.4.6. Fermentation Index

Fermentation index (FI) corresponds to the color change within the bean cotyledons during fermentation. This change is due to the decreasing anthocyanin content as beans progress through fermentation [30]. A 50 mg sample of previously prepared cocoa powder was weighed and mixed with 5 mL MeOH:HCl (97:3 *v*/*v*). Samples were extracted at 4 ◦C for 16–18 h, centrifuged for 5 min at 3500× *g*, and the clear supernatant was collected. Absorbance of the supernatant was read at wavelengths 460 nm and 530 nm using UV-VIS spectrophotometer (model Cary 60 UV-Vis Agilent Technologies Stevens Creek Blvd. Santa Clara, CA, USA). All the measurements were performed in triplicate.

The FI was obtained by calculating the ratio of the absorbance at 460 nm and 530 nm (FI = A460/A530). Values greater than 1 are considered as well-fermented, while less than 1 as under-fermented beans [31,32]. However, it must be noted that this accounts for the Forastero variety and with some precautions for the Trinitario variety (which can contain both purple and white beans). Criollo beans do not contain anthocyanin pigments, therefore FI cannot be used to describe the fermentation level for this variety. In our study, both Trinitario and Forastero beans were used, but white beans were always absent.

#### 2.4.7. pH and Titratable Acidity

Cocoa powder (5 g) was mixed with 100 mL hot water (100 ◦C), stirred, and allowed to stand for 30 min. After 30 min, when the suspension was cooled up to 25 ◦C, it was centrifuged for 10 min at 5000 rpm and vacuum filtered through Whatman No. 4 paper filter according to AOAC 2006 methods 970.21 (pH) and 942.15 (potentiometric titration) [21], AOAC Section 42.104 (16th Ed. 1995) [21,32–35].

The pH of the filtered solution was measured with a pH-meter model PC 80 + DHS (XS Instruments, Carpi, Italy) and then 25 mL aliquots of the same solution were titrated to pH 8.1 with 0.05 M NaOH. All data were measured in triplicate. Titratable acidity results are expressed as mMol NaOH/100 g powder [34] or % acetic acid [21].

It is important to note that this procedure was not for quantifying the actual pH of the cocoa bean itself, but rather to measure the acidity derived from bean acids diffusing into water; it is useful for comparison between the pH of solutions produced by different beans [30].

#### *2.5. Wavelenght Selection and Chemometric Analyses*

Spectral chemometric analyses were performed using firstly the wavelength selection and secondly the full spectra collected. Wavelengths selection was carried out through the interval partial least-square (iPLS) [36] and through the principal component regression (PCR) [37] by using R software version 3.2.5 (R Core Team, Auckland, New Zealand, 2016) and WinISI software (Infrasoft International, Port Matilda, PA, USA), respectively. In particular, the iPLS was carried out applying the forward mode, in which the full spectrum was subdivided in 30 intervals that are successively included in the analysis: the first step calculated 30 models (one for each interval) that were tested using the cross-validation; the interval which provides the lowest model root-mean-square error of cross-validation were selected as most informative. The selected intervals were calculated per each parameter investigated and used for the following modelling. The PCR is based on the identification of the principal factors variance among spectral absorbance data through the principal component analysis [38]. Wavelengths selection was performed on the spectra acquired with FOSS DS-2500 on cocoa powder.

The second approach considered the use of the full spectrum and mathematical treatment as reported by several authors [39–41] in foods for chemical prediction purpose. This procedure takes advantage of the mathematical treatment as multiplicative correction (MSC) of the dispersion used to correct the problems of dispersed light in reflectance spectroscopy or the spectra normalization using standard normal variation (SNV) and first or second derivatives often used to remove the deviation and slope of the baseline in the spectrum [42]. This approach was applied to spectra acquired with both instruments, on both whole and ground cocoa beans.

The calibration models were performed using the Modified PLS (MPLS) regression on wavelength selected and on full spectra, whereas PCR was applied on full spectra (WinISI software, Infrasoft International, Port Matilda, PA, USA). Prediction equations were validated using a 5-fold cross-validation. Samples with a predicted value that differed more than 2.5 SD from the reference value (T-statistics) were considered outliers and removed from the dataset. Several combinations of scatter corrections (NONE, no correction; SNV\_DT, standard normal variate and detrending; MSC, multiplicative scatter correction) and derivative mathematical treatments (0,0,1,1; 1,4,4,1; 2,5,5,1; where the first digit is the number of the derivative, the second is the gap over which the derivative is calculated, the third is the number of data points in the first smoothing and the fourth is the number of data points in the second smoothing) were tested. The performances of the prediction models were evaluated based on the number of the standard error of calibration (SEC), cross-validation (SECV), the coefficient of determination of cross-validation (R2cv) and the ratio performance to deviation of cross-validation (RPDcv) calculated as the ratio between SD and SECV [43]. Predictions were considered excellent when R2 was greater than 0.91, good when R2 ranged from 0.82 to 0.90, approximate when R2 was between 0.66 and 0.81, and poor when R2 was less than 0.66 [44]. Prediction models with RPD greater than 2.5 were considered adequate for analytical purposes [45], whereas prediction models with RPD smaller than 1.5 were considered unsatisfactory [44].

#### **3. Results and Discussion**

#### *3.1. Chemical Properties*

Shell content was on average 13.25% (Table 1), with minimum and maximum values (11.13% and 18.34%, respectively) in line with those reported in the literature (12–20%) [46,47]. Although the shell provides protection to the nib from mold and insects infestations, the shell content should be as low as possible (10–14%) because it has very little commercial value for the cocoa processor: it is removed during cocoa bean processing and it mainly constitutes a waste material [48].

**Table 1.** Descriptive statistics of cocoa beans: SD = standard deviation; CV = coefficient of variation; TPC = total phenolic component; TA = titratable acidity; FI = fermentation index; DM = dry matter.

Dry matter was on average 94.51%, with a minimum of 93.30%. These values correspond to an average moisture content of 5.49% and maximum of 6.70%, which are mainly below the optimal commercial levels of 6.5–8.0% as reported in CAOBISCO/ECA/FCC [19] but are in line with data found in the literature [49]. Moisture is a parameter that depends on storage conditions: since storage conditions of the studied samples varied, this may have affected the final moisture levels.

The average ash content of 2.99% found in our samples was in line with data reported in the literature [48,50].  These data are in line with other studies [21,50]. African cocoa beans have generally higher fat content than American beans [16], but this was not observable in our set of samples. However, according to literature, the fat content can vary greatly from values of about 40 g/100 g to values of 57–58 g/100 g depending on different factors such as: genotype, plant age, growing practices, fermentation, drying processes and environmental conditions [51,52].

FI is one of the most used parameters for determining the degree of fermentation of cocoa beans as an indirect measure of the anthocyanin content [29,35]. In our case study, 22 out of 56 samples had a FI slightly below 1, with a minimum value of 0.57, which would indicate a low fermentation degree. The maximum value was 2.24 and the average was 1.29. The coefficient of variation for this parameter was particularly high (38.43%). Since the FI is an indirect measurement of anthocyanin content, the high dispersion of data might be due to factors other than solely the fermentation degree. It has been reported that different hybrids or genotypes have different pigments and that phenolic compounds are quantitatively affected by cocoa growth conditions (microclimate and position of pods on the tree) [29].

The TPC in the dried fat-free mass of our samples exhibited a wide variation, ranging from 32.58 to 98.04 mg/g dry defatted powder. In fermented beans, TPC should be approximately 5% in the dried fat-free mass, and values above 10% are considered a sign of a bad fermentation [53]. The average value of TPC in our samples was 56.42 mg/g dry defatted powder (equals to 5.6%) that would indicate well-fermented beans. Moreover, few samples showed values close to 10%. Overall, the values are in line with those reported in Anyidoho, et al. [54] and Djikeng, et al. [55].

In dried cocoa beans, a high degree of acidity is usually associated with a pH of 5.0 or less [19]. Some studies report that beans of higher pH (5.5–5.8) are considered unfermented, with a low fermentation index, and result in chocolates with high astringency [32], while beans of lower pH (4.75–5.19) are considered as well-fermented. Other studies report that pH of 5–6 is considered good for flavor development, and cocoa beans with pH below 4.5 are not accepted by cocoa bean processers because they show low levels of flavor precursors, and high acidic-derived products [35]. The pH can still be considered as a good indicator of fermentation as higher pH correlates to a lower fermentation degree [16] and an "international acceptable range" of 5.00–5.55 for dried cocoa beans [56] can be

considered as a valid reference. In our case study, cocoa beans had an average pH of 5.58 with a minimum of 4.84. This describes a situation of well fermented samples.

The titratable acidity value is often associated with the beans' pH. The present results confirm an overall good fermentation of the samples with an average titratable acidity of 17.19 mmol NaOH/100 g powder, in line with data reported in the literature [57,58].

Overall, this set of samples included many variation factors (e.g., genetic variety, crop, fermentation and drying conditions, transport, and storage) giving rise to high coefficients of variation in most of the studied parameters [59].

#### *3.2. Spectral Characteristics of Cocoa Samples*

Figure 1 is representative for average NIR spectra of cocoa beans samples obtained by FOSS DS 2500. The spectra show high similarity with spectra found in the literature [21,27,50,51]. Since cocoa beans contain about 50% of fat (Table 1), absorption spectra are dominated by signals derived from C=O and CH2 groups [49]. The absorptions around 1930 nm are caused by the second overtone vibration of ester C=O and O–H asymmetric stretching [49,60]. Caporaso, et al. [61] reported that wavelength of 1919 nm has been attributed to the C=O stretching second overtone in the carbonyl groups (–CO2H or CONH) but this absorption band is very close to 1923 nm, which is assigned to the O–H group of water and therefore it might be influenced by this group.

ďĞŶĐŚƚŽƉͺǁŚŽůĞ ďĞŶĐŚƚŽƉͺŐƌŽƵŶĚ ƉŽƌƚĂďůĞͺǁŚŽůĞ ƉŽƌƚĂďůĞͺŐƌŽƵŶĚ

**Figure 1.** NIR spectra (mean) of whole (gray) and ground (black) cocoa beans acquired with benchtop spectrometer (FOSS DS 2500) and NIR spectra (mean) of whole (yellow) and ground (green) cocoa beans acquired with portable NIR spectrometer (PoliSPEC-NIR).

The combination vibrations of CH2 stretch and CH2 deformation appear around 2320 nm. Moreover, the absorption at 1744 nm has been previously assigned to C–H stretch first overtone (CH2) of lipids, and the CH2 group also absorbs at 1725 nm, due to the C–H stretch first overtone [61]. Similar wavelength values (i.e., 1750 nm and 1730 nm), associated with first overtones of symmetric and anti-symmetric C–H stretch vibration (CH2-groups), are reported by Krahmer et al. [49].

Fat content is also related to the absorption bands visible around 1200 nm, as reported by Hayati et al. [27]. The authors also argued that the bands in the wavelength regions of 1460–1490 nm and 1920–1980 nm are most likely related to moisture content (O–H bonds). However, absorbances around 1450 nm have been attributed to carbonyl groups (e.g., ketones and aldehydes) as well as O–H polymeric groups, which can be due to complex carbohydrates, and the region between 1400 nm and 1440 nm has also been attributed to aliphatic alcohols and phenols [61].

Absorbance around 1490 nm has been attributed in the literature to several possible chemical bond vibrations, including N–H stretch first overtone and O–H stretch first overtone, thus indicating amides or compounds such as cellulose [61]. Accordingly, Krahmer et al. [49] reported that first overtones of intermolecular H-bridges and stretch vibrations of amidic NH-groups can be observed in the region of 1400 to 1500 nm and the corresponding combination of two amides can be found around 2130 nm.

Barbin et al. [50] associated the broad peaks around 1190, 1460 and 1950 nm with O–H, C–H, N–H stretch first and second overtones and combination bands that can be attributed to water absorption and protein changes.

Peaks around 1215 nm are visible and are associated with –CH=CH second overtone [23] and even C–H stretching second overtone (–CH3 or –CH2) of carbohydrates is associated with this wavelength [61].

The absorbance at 2057 nm indicates an N–H stretch/amide 1st combination band, which has been attributed to protein, while the peaks at 2145 and 2313 nm have been tentatively attributed to C–H deformation and C–H deformation and C–H bend second overtones respectively, both indicating lipids [61].

#### *3.3. Calibration Models for Cocoa Beans Quality*

Variable selection is generally applied in the multivariate analysis to extract the most informative region, removing redundant information. However, among the approaches tested in this study, a lower prediction was observed for the PCR than the MPLS approach as observed in the study of Xie et al. [37]. In detail, in the present study, the PCR showed poor performance of prediction for all traits investigated (see Supplementary Table S2).

Comparing the performance of prediction using the MPLS between full and iPLS selected spectra, it was observed that among the eight parameters, the best prediction was achieved using the full spectra for seven of them (see Supplementary Table S3). The iPLS wavelength selection had a better performance in the fat prediction (R2cv of 0.86 and RPD of 2.88) that did not differ substantially from the prediction obtained using the whole spectrum (900–1680 nm; R2cv = 0.83 and RPD = 2.43).

The results of prediction performance for the benchtop (NIR FOSS DS 2500) and the portable (PoliSPEC-NIR) spectrometers are presented in Tables 2 and 3, which describe data obtained from whole cocoa beans and peeled-ground cocoa beans, respectively.

**Table 2.** Fitting statistics of prediction models for whole cocoa traits developed using cross-validation results for benchtop (NIR FOSS DS 2500) and portable (PoliSPEC-NIR) NIR-spectrometers.

NONE = no correction; SNV\_DET = SNV and detrend; MSC = multiplicative scatter correction; SD = standard deviation of reference data selected; SEcal = standard error in calibration; R2cal = coefficient of determination of calibration; SEcv = standard error in cross-validation; R2cv = coefficient of determination of cross-validation. TPC = total phenolic compound; TA = titratable acidity; FI = fermentation index; DM = dry matter.

**Table 3.** Fitting statistics of prediction models for ground cocoa traits developed using crossvalidation results for benchtop (NIR FOSS DS 2500) and portable (PoliSpec NIR) NIR-spectrometers.

NONE = no correction; SNV\_DET = SNV and detrend; MSC = multiplicative scatter correction; SD = standard deviation of reference data selected; SEcal = standard error in calibration; R2cal = coefficient of determination of calibration; SEcv = standard error in cross-validation; R2cv = coefficient of determination of cross-validation. TPC = total phenolic compound; TA = titratable acidity; FI = fermentation index; DM = dry matter.

Generally, most of the cocoa studies were performed on ground cocoa to reduce the effects of the physical sample properties on spectra collection [24]. Indeed, for both NIRS devices, the best performances of prediction were observed on ground sample, probably due to the enhanced homogeneity of the samples characterized by a similarity in the particles size and in a more compacted powder that affects the scattering of light.

In this study, spectra corrections by mathematical treatments to remove irrelevant data such as noise and background information were evaluated. In particular, SNV and MSC were used as pre-processed methods to remove the influence of solid particle size and the surface scattering; moreover, the methods above are mainly recognized as the best mathematical treatment in the equation models developed for whole cocoa. The SNV\_DT and MSC treatments improved the prediction accuracy for some quality parameters of both whole and ground cocoa bean samples, while for other parameters raw spectra gave the best results. This was in line with Barbin et al. [50] who found no considerable improvement of the predictive ability when comparing different pre-processing methods with the original raw data. Indeed, Barbin et al. [50] stated that since the complexity of the models was similar to that obtained with the original data, it is feasible to use the raw spectra to build prediction models for both whole beans and ground cocoa samples.

Moreover, to evaluate the performance of technologies on the market, the whole spectrum was considered to perform the prediction equations, although some researchers suggest that selection of spectral intervals could lead to higher prediction performances [35,62]. All the predictions performed against whole bean sample can be considered as approximate to poor [44] with the highest capability achieved for DM (R2cv = 0.72; RPDcv = 1.86) for the benchtop and for pH (R2cv = 0.70; RPDcv = 1.83) with portable device (Table 2). In general, the minor prediction capability in whole cocoa beans compared to the ground sample has been confirmed also in the study of Hernández-Hernández et al. [63], in which the poor performance of chemical predictions was attributed to the shell that reflects the incident light hindering the interaction with internal constituents. Although predictions on whole cocoa beans were not adequate for quantitative purposes, they could represent a fast

approach for food business operators to sort cocoa beans towards a specific transformation according to high or low value. Moreover, at germplasm banks and breeding programs, a rapid whole cocoa analysis reduces the time required for the shell removing (usually carried out by hand in the laboratory), suggesting NIRS devices are capable to identify functional genotypes to improve qualitative aspects in cocoa products [63].

Excellent performance was obtained in ground cocoa for protein content (R2cv = 0.91; RPDcv = 3.40) and very good prediction was achieved for DM (R2cv = 0.90; RPDcv = 3.20), ash (R2cv = 0.89; RPDcv = 2.98), pH (R2cv = 0.88; RPDcv = 2.96) and TA (R2cv = 0.86; RPDcv = 2.70) using the NIR FOSS DS 2500 spectrometer (850–2500 nm) (Table 3). The PoliSPEC-NIR spectrometer (900–1680 nm) had the best predicting performances for fat content (R2cv = 0.82; RPDcv = 2.34) in ground samples; whereas, for the other traits, the portable device showed lower performances compared to the benchtop (Table 3).

To deeper investigate if the divergences between the devices might depend on the different spectral range used, a further prediction equation was performed for the benchtop using the same spectral range (900–1680 nm, every 2 nm) of the portable tool. In the comparison with the performance obtained considering the whole spectrum, a greater performance of predictions was observed for ash (R2cv = 0.90; RPDcv = 3.20), protein (R2cv = 0.93; RPDcv = 3.84), DM (R2cv = 0.94; RPDcv = 4.16), and lipids (R2cv = 0.83; RPDcv = 2.43). However, although the TPC remained unpredictable, an increment was observed in the new prediction equation (R2cv = 0.46; RPDcv = 1.37). Although a good predictive capability was maintained, lower performance prediction was observed for TA (R2cv = 0.85; RPDcv = 2.60) and pH (R2cv = 0.82; RPDcv = 2.34).

Thus, to comprehend the origin of the performance divergences between devices, the component loadings were developed for each tool to assess and compare the interactions between wavelengths and functional groups (Figure 2). The loading plots permit to better understand which wavelengths are more informative for a specific trait variability, showing the range which is mostly considered to develop the model. A strong similarity between portable and benchtop devices were overall observed for chemical parameters directly quantified.

In particular, although the same ranges and performance of prediction (R2cv = 0.83) were obtained in both devices for lipid loading plot, the highest loadings were observed in the spectral region between the 1212 and 1232 nm and 1368 and 1398 nm for portable and benchtop, respectively.

Such association between those range and lipid variability has been confirmed by [64] in cereal food products. Similar patterns for the protein loading plot were observed between the two devices; however, the high loadings observed between 1200 to 1400 nm were related to C–H second overtone and N–H stretching first overtone of protein, respectively [65,66]. Moreover, a high loading was observed around 1100 nm exclusively for the benchtop device; this is probably due to the higher sensitivity of the device that is reflected in the best performance of prediction (R2cv = 0.93) the range 1100–1400 nm being considered as an essential spectral region for the protein quantification analysis [67].

A comparable loading plot was also observed for pH in which the highest trait variability was explained by the 910 [68] and 1398 nm for both devices. Divergences in titratable acidity loading patterns were found; however, the most informative wavelengths (930–950; 1106; 1390–1400 nm) are related to the second combination region of the carboxylic acids [69]. The loading plot of DM showed notable peaks between 1200–1224 and 1373–1394 nm, mainly related to the water [69].

**Figure 2.** Loadings for the first principal component of fat, protein, pH, titratable acidity (TA), total phenolic compound (TPC), fermentation index (FI), dry matter (DM), and ash for NIR spectra of the ground cocoa samples for DS 2500 (blue line) and PoliSPEC NIR (green line).

Ash being an inorganic matter cannot be directly detected by NIRS; its amount is indirectly measured by the association with organic bonds, thus the loadings plot and the highest variability observed for ash is 1200 nm and 1376 nm for the benchtop, and 1396 nm for the portable device account for other organic components. Otherwise, loadings plots observed for TPC and FI were not strictly related to a specific spectrum range, probably due to the lower variability collected with the samples considered. In general, the performance divergences between the two NIRS devices could be explained by the difference in the detector equipment; in detail, the semiconductors included in portable (PoliSPEC-NIR) and benchtop (NIR FOSS DS 2500) devices are Indium gallium arsenide (InGaAs) and silicon lead sulfide, respectively, which affect the spectral response and the prediction capability Lin, et al. [70].

In our study, the accuracy of prediction for both FI and TPC was not satisfactory for any of the instruments and for both whole and ground cocoa bean samples. The influence of variable fermentation degrees of cocoa samples can be crucial in the prediction of FI and TPC, which are strictly related to the fermentation level of cocoa beans. Sunoj, Igathinathane and Visvanathan [32] showed how factors such as pod storage duration (before the fermentation process), and fermentation time, had a significant effect on the fermentation index, which was seen to increase together with the increment of these two parameters. The authors argued that these parameters are indirectly affected by the samples' chemical composition, thus the accuracy of prediction models are generally lower than those reported for major components. The reason might fall on the fact that our samples included only commercial cocoa beans which were supposed to be well-fermented, although with some natural variations, thus reducing the variability for the TPC and FI. Moreover, there could have been a negative influence of lipid absorbances in the models for TPC: fat has been indicated as a disturbance factor as beans with higher relative fat content have lower non-fat solids, where polyphenols are concentrated [61].

Although the FI was not correctly predicted by the constructed models, the estimations in ground samples of parameters related to correct fermentation such as pH and TA were approximative and good with the portable and benchtop devices, respectively, in line with previous results [25,49]. This method could provide a rapid and low-cost multiparametric analysis for cocoa evaluation. Portable instruments are usually less expensive than benchtop solutions (about a fifth) [71], and the cost of analyses are mainly related to the development and upgrade of calibration curves. Moreover, compared to wet analyses, through the application of spectrometric methods the cost of the analytical determination is drastically reduced as the number of examined samples increases.

The presented prediction models might be the basis for an overall cocoa bean quality evaluation based on NIR spectra. However, despite the presented parameters being good indicators of cocoa bean quality, a grading classification of cocoa beans' was beyond the scope of the present work, as it would require the investigation of other indicators, also related to the sensorial profile of the beans, as reported in previous studies on cocoa quality indexes (CQI) [72,73].

#### **4. Conclusions**

The results of this paper demonstrated that NIRS portable and benchtop devices coupled with chemometrics methods could be adopted for the chemical evaluation of commercial cocoa beans. The performances of predictions are affected by the presence of shell and the sample particle sizes of cocoa beans. The current study has successfully demonstrated that NIR, as a nondestructive analytical method, can be considered as rapid and reliable option to traditional methods to quantify lipids, protein, pH, titratable acidity, dry matter and ash in cocoa ground beans.

The NIRS benchtop instrument provided better performance of quantification considering the whole (800–2500 nm) and the reduced spectrum (900–1680 nm) than the portable device. Variable selection through iPLS or PCR did not improve prediction models compared to full spectra analyses. Benchtop instrument showed excellent prediction capability

in DM (R2cv = 0.94), protein (R2cv = 0.93) and ash (R2cv = 0.90), whereas lipids (R2cv = 0.83), TA (R2cv = 0.86) and pH (R2cv = 0.88) were well predicted on ground beans considering wavelengths between 900–1680 nm. Those results indicate that models developed for benchtop devices are applicable for cocoa quality control as an excellent option to substitute conventional methods.

On the other hand, the NIRS portable device showed lower but valuable performance of prediction than benchtop spectrometer. The prediction obtained for handheld device represents an appealing strategy for food business operators to apply in the field to control and check the product in every phase of trade and transportation, and also to segregate whole cocoa beans targeted to a specific transformation in different supply chains.

Based on these results, further studies including a wider variability of fermentation phases, cocoa bean varieties and origins as well as additional production steps of the cocoa supply chain could be investigated to support the fair-trade cocoa sector.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/xxx/s1, Table S1: Cocoa origin of different commercial lot analysed in this study Table S2: Fitting statistics of prediction models for ground cocoa traits developed using full spectra and principal component regression (PCR) and cross-validation results for benchtop (NIR FOSS DS 2500); Table S3. Fitting statistics of prediction models for ground cocoa traits developed using selected wavelengths through the interval PLS (iPLS) and cross-validation results for benchtop (NIR FOSS DS 2500).

**Author Contributions:** Conceptualization, P.C., M.F., K.D. and L.F.; methodology, D.V.d.W., K.D., M.M.; formal analysis, M.F., S.C. and M.M.; resources, P.C. and L.F.; data curation, M.F., S.C. and M.M.; writing—original draft preparation, M.F. and S.C.; writing—review and editing, S.C., D.V.d.W., K.D., L.F. and P.C.; supervision, P.C. and L.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** All related data and methods are presented in this paper. Additional inquiries should be addressed to the corresponding author.

**Acknowledgments:** The authors gratefully acknowledge Roberta Marcuzzi for the valuable help in performing the analyses.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.


*Article* **Weed Infestation and Health of the Soybean Crop Depending on Cropping System and Tillage System**

**Dorota Gaw ˛eda 1 , Małgorzata Haliniarz 1,\* , Urszula Bronowicka-Mielniczuk 2 and Justyna Łukasz 1**

Received: 16 April 2020; Accepted: 5 June 2020; Published: 7 June 2020

**Abstract:** This study evaluated weed infestation and health of the soybean crop grown in crop rotation (CR) and monoculture (CM) under conventional tillage (CT) and no-tillage (NT) conditions. The research proved that growing soybean in monoculture and under no-tillage conditions increases weed infestation and infection of soybean with fungal diseases. In these treatments, increased numbers of most of the dominant species were also found. A significantly higher percentage of monocotyledonous species and a much lower percentage of dicotyledonous ones in total weed dry weight were shown in the CR treatment relative to CM and in the NT system compared to CT. The biodiversity of the weed community was similar in monoculture and crop rotation, and slightly greater in the NT system in comparison with CT conditions. In both tillage systems, *Amaranthus retroflexus* was the weed species that most infested the soybean crop. In soybean grown after itself, *Amaranthus retroflexus* was the weed that occurred in the greatest numbers, while, in crop rotation, this was *Echinochloa crus-galli*. In all years of the study, soybean was infected with *Septoria glycines* to the highest degree, which was followed by *Cercospora sojina*, whereas infection with *Ascochyta* sp. was the lowest. Weather conditions in individual years of the study were proven to affect weed infestation and infection of soybean with fungal diseases. The study results prove that cropping systems and tillage systems significantly affect weed infestation and health of the soybean crop.

**Keywords:** *Glycine max* (L.) Merr.; cropping system; tillage system; weed infestation; biological diversity; fungal diseases

#### **1. Introduction**

Soybean is one of the world's leading crops and, at the same time, the most important *Fabaceae* species. It is characterized by a very valuable chemical seed composition because soybeans contain 18–22% of oil with a high content of unsaturated fatty acids and 33–45% protein with an excellent amino acid composition [1,2]. Due to this, the soybean finds a very wide application in human and animal nutrition, and it is a raw material for many branches of industry. Since soybeans can be grown with minimal use of chemical protection (primarily herbicides) and with reduced nitrogen fertilization, under Polish conditions, soybean cultivation is environmentally-friendly and largely compatible with integrated crop protection [2].

In Poland, the general principles of integrated crop protection are governed by the provisions of the Act of 8 March 2013 on crop protection products (Dz. U. (Journal of Laws) item 455) and the Regulation of the Minister of Agriculture and Rural Development of 18 April 2013 regarding requirements for integrated crop protection (Dz. U. (Journal of Laws) item 505) [3]. In accordance with the currently applicable principles of integrated crop protection, a great role is ascribed to natural fertility of soil and its biological activity. Therefore, the number of ploughings and their depth should be reduced and other implements that deeply loosen the soil without turning it over should be used instead of the plough [2]. The destructive impact of conventional plough tillage on the structure of the topsoil layer and reduced soil biological diversity, among others, make an argument for its abandonment [4,5]. When selecting a tillage system, the specific conditions of a particular farm should be taken into consideration in order to ensure optimal plant growth and developmental conditions [6,7]. Cultural practices affect the amount of crop residue remaining on the soil surface, which, in consequence, determines the growth of pests and weeds as well as the development of diseases [8,9]. Exact mixing of crop residue on which crop pathogens could have survived with soil is considered to be one of the tasks of conventional tillage [10]. On the other hand, an improvement in soil quality, the level of microbial activity, nutrient cycling, and microbial diversity that can be achieved by using no-tillage systems can significantly determine natural resistance of plants to diseases [11].

It is particularly justified to use no-tillage in soybean under rainfall deficit conditions during a growing season. The yield potential of this legume crop is substantially limited by climatic conditions, predominant temperature, and the amount and distribution of rainfall [12,13]. Therefore, in areas with low rainfall levels, it is important to use cultural practices that will ensure retention of the greatest possible amount of rainwater. Agronomic practices that involve replacing the plough with implements and do not turn the soil over offer such a possibility. Thus, no-tillage performs best in a dry climate or in a climate with an uneven distribution of rainfall where crop yields are frequently equal to or higher than in the case of conventional tillage [14].

A correctly designed crop rotation is also the basis for the integrated crop protection system. Monoculture impoverishes both organic matter and soil microbial life [15,16], which leads to increased weed infestation, and deteriorates the phytosanitary condition of a crop [17–19]. Due to the fact that, under Polish conditions, soybeans are not severely attacked by dangerous diseases, an attempt was made to investigate its response when it is grown a in short-term monoculture.

The aim of the present study was to evaluate the effect of the cropping system and tillage system on weed infestation and health of the soybean crop.

#### **2. Materials and Methods**

#### *2.1. Location of the Experiment and Soil and Climatic Conditions*

This study was conducted at the Uhrusk Experimental Farm (51◦18′11′′ N, 23◦36′50′′ E) over the period of 2014 to 2017. A field experiment was set up on Rendzic Phaeozem [20] with the grain-size distribution of sandy loam. The soil on which the experiment was carried out was characterized by alkaline pH (in 1 M KCl = 7.7), very high phosphorus availability (229.8 mg P kg−1 soil), high potassium availability (150.2 mg K kg−1 soil), and very low magnesium availability (16 mg Mg kg−1 soil). The humus content was 1.5%, whereas the percentage of fine particles (<0.02 mm) in the 0–30 cm layer amounted to 20.7%.

Throughout the duration of the experiment, all growing seasons of soybeans were characterized by a higher mean air temperature and rainfall total than the long-term mean (Figures 1 and 2).

66

**Figure 1.** Mean monthly air temperature (◦C) at the Bezek Meteorological Station from 2014 to 2017.

**Figure 2.** Total rainfall and rainfall distribution (mm) at the Bezek Meteorological Station from 2014 to 2017.

#### *2.2. Experimental Design and Agronomic Practices*

The soybean cultivar 'Merlin' was grown in an experimental area of 768 m2 (area of all crops in crop rotation) and 192 m2 (area of soybean in monoculture). The total area of the soybean in each year of the experiment was 384 m2 (soybeans in crop rotation had a total area of 192 m2 and soybeans in a monoculture had a total area of 192 m2 ), while the single plot area was 32 m2 . A field experiment was set up as a split-block design in triplicate. Two experimental factors were included in this study.

The experiment diagram shows the distribution of the cropping system and tillage systems and the place of soybeans in crop rotation during individual years of the experiment (Table 1). In each year of the research, soybean was sown in a different crop rotation field after winter wheat. Levels of one factor (cropping system involving crop rotation and monoculture) are arranged in rows, and levels

of the second factor (tillage system involving conventional tillage and no-tillage) are arranged in columns. The levels of both factors arranged perpendicularly are crossed to form a combination of these factors. Random levels of one factor determined sub-blocks for levels of the other factor and vice versa. Therefore, the accuracy of assessing the effects of both factors is the same. In all years of research, conventional tillage (CT) and no-tillage (NT) was used for all plants sown in crop rotation (CR) and the soybean monoculture (CM).

**Table 1.** Experiment scheme. \*Soybean location in the experiment. CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage.

crop rotation: soybean—winter wheat—winter oilseed rape—winter wheat.

Under the conventional tillage system, the following tillage operations were carried out: skimming + harrowing, harrowing, autumn ploughing, spring: harrowing, NPK fertilizers application, cultivating with harrowing, seeding, and harrowing. Under the no-tillage system, the following tillage operations were done: stubble cultivator: grubber + cage roller instead of skimming and cultivating instead of autumn ploughing, and spring: the same tillage operations as under the conventional tillage system.

Mineral NPK fertilization was applied before sowing soybean and fertilizer rates were determined by taking into account the crop's nutritional requirements and soil nutrient availability. Fertilization was applied at the following rates: N—30 kg ha−1 (34.5% ammonium nitrate), P—40 kg ha−1 (40% superphosphate), and K—80 kg ha−1 (60% potassium salt).

In the years 2014 and 2016, the sowing time was in the last 10 days of April, whereas, in 2015 and 2017, the sowing time was in the first 10 days of May. Each time before sowing, seeds were dressed with *Bradyrhizobium japonicum* bacteria and the seed dressing Vitavax 200 FS (carboxin, thiuram) at a rate of 80 g and 80 g (active ingredients) per 100 kg of seeds. Seeds were sown at a depth of 3 cm, a row spacing of 20 cm, and with a planned density of 100 plants per 1 m2 .

Weed management in the soybean crop involved mechanical and chemical weed control. Immediately after seeding, a mixture of the following herbicides was applied: Afalon Dyspersyjny 450 SC (linuron) + Dual Gold 960 EC (metolachlor-S) at a rate of 450 g + 1728 g ha−1 (active ingredients). Mechanical weed control included harrowing 3–4 days after seeding and double harrowing after emergence of soybeans (at the first trifoliate leaf stage—BBCH 12, and at the third trifoliate leaf stage—BBCH 13).

Soybeans were harvested at full maturity of soybeans (BBCH 89) in the second 10 days of September in 2014 and 2016, while, in 2015 and 2017, soybeans were harvested at full maturity in the third 10 days of September.

#### *2.3. Scope of Study and Statistical Analysis*

The evaluation of the species' composition of the weed community as well as of weed density and dry weight, which was performed by the dry-weight-rank method at the pod and seed ripening stage (BBCH 81/82). The sampling area was delineated by a quadrat frame with the dimensions of 1 m × 0.5 m in two randomly selected points of each plot. Weed species nomenclature followed Mirek et al. [21].

The Shannon-Wiener diversity index (*H*′ ) and the Simpson dominance index (*SI*) were calculated according to the following formula [22,23].

$$H' = -\Sigma \text{ Pi } \ln \text{Pi} \tag{1}$$

$$SI = \Sigma \text{ Pi } \mathbf{2} \tag{2}$$

where:

Pi—probability of species occurrence in a sample,

Pi = n/N,

n—number of individuals in species,

N—total number of individuals in the sampling area,

ln—natural logarithm.

The higher the value of the Shannon-Wiener index, the greater the diversity of a community. The range of Simpson index values is from 0 to 1 with values close to 1 indicating a clear dominance of one or several species.

The evaluation of soybean health was carried out at the first pod set stage (BBCH 70) on 50 plants from each plot. The determination was made using a five-point scale (0–4) expressing increasing plant infection, where 0 denotes leaves without disease symptoms, 1 denotes up to 10% of leaf surface infected, 2 represents up to 50%, 3 represents up to 75%, and 4 represents 100% of leaf surface infected. Having established the degree of plant infection, the number of plants within a specific degree of infection was determined for all replicates. Next, the disease index was calculated according to McKinney's formula [24].

$$\text{Wch} = (\sum \text{a}) (\sum \text{b}) \times 100 \tag{3}$$

where:

*Wch*—disease index;

Σa—sum of the numeric indices in the scale multiplied by the number of plants corresponding to a given index;

Σb—total number of examined plants multiplied by the highest index in the scale.

The study results collected over the period 2014–2017 were analyzed using two-way analysis of variance with repeated measurements over the four growing periods. The significance of differences was estimated by Tukey's test at a significance level of α = 0.05. ANALWAR–5.3.FR statistical software was used for calculations. The distribution conformity with normal distributions was verified with the Shapiro-Wilk test, while homogeneity of the variance was tested with the Bartlett test. When necessary to homogenize the variance, the data were subjected to angular transformation before the variance analysis [25]. Before performing the statistical calculations, the percentage values were transformed using the following equation.

$$y = \arcsin\sqrt{x} \tag{4}$$

#### **3. Results**

#### *3.1. Weed Infestation of Soybean Crops*

Cropping systems and tillage systems caused significant differences in weed infestation of the soybean crop (Figures 3 and 4). On average over the study period, in the soybean monoculture, the number and dry weight of weeds was, respectively, higher by 69.4% and 28.6% than in crop rotation. The significant interactions between the cropping system and the tillage system were not found (Figures 5 and 6). The weed of dry weight did not differ between both tillage systems only in the first year of the experiment. The highest weed of dry weight was found in 2014 in the soybean monoculture both under plough tillage (CT) and no-tillage (NT) conditions. Weed infestation of the soybean crop expressed as dry weight of weeds was highest in the first year of the experiment (2014), which was characterized by a higher amount of precipitation than the other years of the experiment

and the long-term mean, especially in May and June. The lowest number and dry weight of weeds were found in 2015 and 2016 (Figures 3 and 4).

≤ **Figure 3.** Mean number of weeds in soybean crop depending on cropping system, tillage system, and years of research. CR—crop rotation, CM—monoculture, CT—plough tillage, and NT—no-tillage. Different letters denote significant differences (*p* ≤ 0.05) among different cropping systems (CR and CM), tillage systems (CT and NT), and years of research. The same letter means it is not significantly different. ≤

≤ ≤ **Figure 4.** Air-dry weight of weeds in soybean crop depending on cropping system, tillage system, and years of research. CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different letters denote significant differences (*p* ≤ 0.05) among different cropping systems (CR and CM), tillage systems (CT and NT), and years of research. The same letter means it is not significantly different.

The statistical analysis revealed that the studied factors significantly modified the percentage of monocotyledonous and dicotyledonous species in total weed dry weight (Figure 7). In crop rotation (CR), the percentage of monocotyledonous species was significantly higher, whereas the percentage of dicotyledonous ones was much lower than in the soybean monoculture (CM). The NT system significantly increased the percentage of monocotyledonous species and decreased the percentage of dicotyledonous ones in total weed dry weight in comparison with the CT system. As far as the interactions between the experimental factors are concerned, it was proven that the percentage of monocotyledonous species in total weed dry weight was lower in the monoculture under conventional tillage conditions, while the percentage of dicotyledonous ones was higher relative to the other experimental treatments (Figure 8).

≤ **Figure 5.** Interactive dependencies of the cropping system and tillage system in shaping the number of weeds in soybean crop (mean for 2014–2017). CR\_CT—crop rotation and plough tillage, CR\_NT—crop rotation and no-tillage, CM\_CT—monoculture and plough tillage, CM\_NT—monoculture and no-tillage. The same letter means it does not have a significantly different (*p* ≤ 0.05) interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT). ≤

≤ **Figure 6.** Interactive dependencies of the cropping system and tillage system in shaping air-dry weight of weeds in soybean crop (mean for 2014–2017). CR\_CT—crop rotation and plough tillage, CR\_NT—crop rotation and no-tillage, CM\_CT—monoculture and plough tillage, CM\_NT—monoculture and no-tillage. The same letter means it is not significantly different (*p* ≤ 0.05) in terms of the interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT).

The agrophytocenosis of the soybean crop grown after winter wheat (CR) included 22 weed species out of which 18 belonged to annual weeds (81.8%) (Table 2). Four perennial weed species and 17 annual ones, which accounted for 81.0% of the total number of species, were observed in a soybean monoculture (CM). In crop rotation, *Echinochloa crus-galli* was the most numerous taxon, followed by *Amaranthus retroflexus*, whereas, in a soybean monoculture, *Amaranthus retroflexus* was predominant.

The number of *Amaranthus retroflexus*, *Chenopodium album*, *Avena fatua*, and *Solanum nigrum* was significantly higher in monoculture (CM) than in CR. Soybean cultivation in crop rotation (CR) also significantly reduced the numbers of the perennial species *Elymus repens*. In CR, *Anchusa arvensis, Veronica persica*, *Viola arvensis,* and *Convolvulus arvensis* were found to be present, but they did not inhabit soybeans grown after itself (CM). *Euphorbia helioscopia, Thlaspi arvense,* and *Plantago major* infested only a soybean monoculture.

≤ ≤ **Figure 7.** Percentage content of weight of monocotyledonous and dicotyledonous weeds in total weed dry weight depending on the cropping system and tillage system (mean for 2014–2017). CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different lowercase letters denote significant differences (*p* ≤ 0.05) of monocotyledonous weeds among different cropping systems (CR and CM) and tillage systems (CT and NT). Different capital letters denote significant differences (*p* ≤ 0.05) of dicotyledonous weeds among different cropping systems (CR and CM) and tillage systems (CT and NT). ≤ ≤

≤ ≤ ≤ ≤ **Figure 8.** Percentage content of monocotyledonous and dicotyledonous weeds weight in total weed dry weight depending on the interaction of the cropping system and tillage system (mean for 2014–2017). CR\_CT—crop rotation and plough tillage, CR\_NT—crop rotation and no-tillage, CM\_CT—monoculture and plough tillage, and CM\_NT—monoculture and no-tillage. Different lowercase letters denote significant differences (*p* ≤ 0.05) of monocotyledonous weeds for interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT). Different capital letters denote significant differences (*p* ≤ 0.05) of dicotyledonous weeds for interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT).

**Table 2.** Species composition of weeds per 1 m2 in soybean crop depending on the cropping system and tillage system (mean for 2014–2017).

CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different letters denote significant differences (*p* ≤ 0.05) of the number of weed species among different cropping systems (CR and CM) and tillage systems (CT and NT). The same letter means it is not significantly different.

A total of 22 weed species occurred in the soybean crop grown under conventional tillage (CT), whereas, in the no-tillage (NT) treatment, 24 species occurred (Table 2). *Anchusa arvensis, Veronica persica*, and the perennial species *Convolvulus arvensis* were found in the NT treatment, but they did not infest soybeans grown under the conventional system. In the CT treatment, on the other hand, the species *Thlaspi arvense* was observed, which was not found in the NT treatment. *Amaranthus retroflexus* occurred in the greatest numbers in both tillage systems. In the NT treatment, the number of *Avena fatua* and *Solanum nigrum* significantly increased compared to CT.

In crop rotation, 22 weed species were found under no-tillage conditions, while, in the conventional tillage treatment, there were six fewer species (Table 3). The following annual weeds: *Anchusa arvensis, Lamium amplexicaule, Veronica persica*, and perennial weeds: *Convolvulus arvensis, Elymus repens, Sonchus arvensis*, did not occur in soybeans grown in crop rotation under CT, but they were found in the non-tilled plots. In the soybean monoculture, greater weed species' richness (21 species) was observed in the CT treatment where *Lamium amplexicaule, Thlaspi arvense*, and *Sonchus arvensis* were found to be present, which are species that did not occur in the non-tilled plots (NT). Both in crop rotation and soybean monoculture, conventional tillage slightly reduced the number of the most numerous weed species. In crop rotation under CT conditions, the number of individuals of the dominant taxon, *Echinochloa crus-galli*, was 25.0% lower than under no-tillage (NT). However, these differences were

not statistically significant. In the soybean crop grown in the monoculture, conventional tillage (CT) slightly reduced the number of individuals of the most numerous species, *Amaranthus retroflexus*.

**Table 3.** Interactive dependencies of the cropping system and tillage system in shaping weed species' composition per 1 m2 in soybean crops (mean for 2014–2017).

CR\_CT—crop rotation and plough tillage, CR\_NT—crop rotation and no-tillage, CM\_CT—monoculture and plough tillage, CM\_NT—monoculture and no-tillage. Different letters denote significant differences (*p* ≤ 0.05) of the number of weed species for an interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT).

The evaluation of the biological diversity of the segetal flora showed that the Shannon-Wiener index (H′ ) and the Simpson index (SI) did not differ significantly in crop rotation (CR) and a soybean monoculture (CM). The soybean crop grown in the no-tillage (NT) system was characterized by slightly greater diversity of the weed community than under CT. This is indicated by the higher value of the Shannon diversity index and a slightly lower value of the Simpson dominance index (Figure 9).

The interaction of the cropping system and tillage system did not cause significant variations in the Shannon-Wiener diversity index and the Simpson dominance index (Figure 10). The agrophytocenosis of the soybean crop grown in crop rotation in the NT treatment was characterized by the greatest diversity, while the lowest one was found in the case of crop rotation and CT conditions. Nonetheless, the difference in the values of the diversity and dominance indices between the individual treatments was not significant.

a

a a a

≤ ≤ **Figure 9.** Shannon-Wiener's diversity index (*H*′ ) and Simpson's dominance index (*SI*) of the weed community in soybean crop depending on the cropping system and tillage system (mean for 2014–2017). CR—crop rotation, CM—monoculture, CT—plough tillage, and NT—no-tillage. Different lowercase letters denote significant differences (*p* ≤ 0.05) of Shannon-Wiener's diversity index (*H*′ ) among different cropping systems (CR and CM) and tillage systems (CT and NT). Different capital letters denote significant differences (*p* ≤ 0.05) of Simpson's dominance index *(SI)* among different cropping systems (CR and CM) and tillage systems (CT and NT). ≤

0.0 CR\_CT CR\_NT CM\_CT CM\_NT  *H' SI* **Figure 10.** Interactive dependencies of the cropping system and tillage system in shaping Shannon-Wiener's diversity index (*H*′ ) and Simpson's dominance index (*SI*) of the weed community in soybean crop (mean for 2014–2017). CR\_CT—crop rotation and plough tillage, CR\_NT—crop rotation and no-tillage, CM\_CT—monoculture and plough tillage, CM\_NT—monoculture and no-tillage. Different lowercase letters denote significant differences (*p* ≤ 0.05) of Shannon-Wiener's diversity index *(H*′ *)* for interaction among crop rotation, monoculture, plough tillage (CT), and no-tillage (NT). Different capital letters denote significant differences (*p* ≤ 0.05) of Simpson's dominance index *(SI)* for an interaction among crop rotation (CR), monoculture (CM), plough tillage (CT), and no-tillage (NT).

#### *3.2. Infection of Soybean Plants with Diseases*

In every year of the study, soybeans were infected with Ascochyta blight (*Ascochyta* sp.), Septoria leaf spot (*Septoria glycines*), and frogeye leaf spot (*Cercospora sojina*). The severity of diseases observed was dependent on the factors studied, but it was generally low in the 2015 growing season characterized by the lowest rainfall level. The growing seasons in 2014 and 2017, during which the

highest amount of rainfall was recorded, promoted the growth of fungal pathogens. During these seasons, infection of soybean with Ascochyta blight, Septoria leaf spot, and frogeye leaf spot was found to be the highest. In 2017, the severity of symptoms of all diseases observed was significantly higher than in 2015 and 2016. Soybean plants were infected with the pathogen *Septoria glycines* to the greatest degree, which was followed by *Cercospora sojina*, while infection with *Ascochyta* sp. Was the lowest (Figures 11–13).

≤

≤

≤ **Figure 11.** Mean infection of soybean with Ascochyta blight (*Ascochyta* sp.) depending on the cropping system, tillage system, and years of research. CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different letters denote significant differences (*p* ≤ 0.05) among different cropping systems (CR and CM), tillage systems (CT and NT), and years of research. The same letter means it is not significantly different.

≤ **Figure 12.** Mean infection of soybeans with Septoria leaf spot (*Septoria glicynes*) depending on the cropping system, tillage system, and years of research. CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different letters denote significant differences (*p* ≤ 0.05) among different cropping systems (CR and CM), tillage systems (CT and NT), and years of research. The same letter means it is not significantly different.

≤

≤

≤ **Figure 13.** Mean infection of soybeans with frogeye leaf spot (*Cercospora sojina*) depending on the cropping system, tillage system, and years of study. CR—crop rotation, CM—monoculture, CT—plough tillage, NT—no-tillage. Different letters denote significant differences (*p* ≤0.05) among different cropping systems (CR and CM), tillage systems (CT and NT), and years of research. The same letter means being not significantly different.

On average, over the four-year study period, the experimental factors were proven to influence the health of soybean plants (Figures 11–13). In the monoculture, the severity of infection with the pathogens causing Ascochyta blight, Septoria leaf spot, and frogeye leaf spot was higher by, respectively, more than three times, almost two times, and nearly three times when compared to crop rotation. The no-tillage system promoted the severity of all soybean diseases observed in this experiment. It was proven that, under the CT system, plant infection by the pathogens *Ascochyta* sp., *Septoria glycines*, and *Cercospora sojina* was lower by 51.9%, 32.4%, and 43.4%, respectively, than under no-tillage (NT).

#### **4. Discussion**

Weeds are strong competitors for soybeans. With heavy weed infestation, plants exhibit nutrient deficiency symptoms and are pale green, while seed yield is lower and of worse quality. Therefore, the profitability of cultivation of this legume crop is greatly dependent on effective elimination of weed infestation [2]. Weed infestation of crops can be effectively reduced, among others, by selecting an appropriate position of a given crop in crop rotation [26]. The study presented in this paper proved that growing soybeans after winter wheat as a previous crop reduces weed infestation when compared to what was determined in the monoculture. The results of a study by Cardina et al. [27] reveal that monoculture cropping promotes the accumulation of weed seeds in the soil. In the study of these authors, the number of weed seeds was also affected by the interaction between tillage and crop rotation since the highest seed density was shown in the monoculture under NT conditions. Our research did not prove the interaction between the cropping system and tillage system impacted weed infestation of the soybean crop, but a slightly higher number of weeds was observed in the soybean monoculture under NT conditions when compared to that found in the other experimental treatments.

The results of studies regarding the effect of tillage systems on weed infestation of crops are not unequivocal. Santín-Montanyá et al. [28] did not prove conventional tillage and no-tillage systems to have a significant impact on the number of weeds in the winter wheat crop. However, the results of these authors' research, similarly to those presented in our paper, show that weed community diversity, as expressed by the Shannon index, increased under the NT system. Sebayang and Rifai [18], on the other hand, found that the dry weed weight in the soybean crop did not differ significantly between conventional tillage, reduced tillage, and no-tillage. Nonetheless, many authors point out that the

no-tillage system increases weed infestation of crops [29–32]. Our experiment also demonstrated a significant increase in the number and weight of weeds under the NT system in comparison with CT. Such an increase may be due to the accumulation of freshly shed weed seeds in the topsoil layer where they germinate and sprout in great numbers in the succeeding crop [33–35]. As a matter of fact, the tillage system does not affect the seed bank size but changes both the composition and distribution of diaspores in the soil profile [36]. The study by Cardina et al. [27] revealed that the number of weed seeds in the soil was highest under NT and generally decreased with increasing tillage intensity. These authors also proved that, under the NT system, weed seeds accumulated near the soil surface (from 0 to 5 cm) whereas, under the other tillage systems, they were evenly distributed across the entire tilled layer. In the opinion of Małecka et al. [37], increased weed infestation under the NT system can generally be observed in the first crop rotation cycle, whereas, during the subsequent period, weed infestation stabilizes and even some positive aspects of no-till field preparation are noticeable.

In the opinion of Chovancova et al. [38], Małecka-Jankowiak et al. [39], and Mancinelli et al. [40], the tillage system modifies soil properties, and, thus, affects plant growth and development. Its effect can be a change in the species' composition of weeds growing in a crop, as shown, among others, by the research presented in this paper. Velykis and Satkus [41] also proved that no-tillage caused a change in the weed species' composition in comparison with the conventional system. In particular, it increased the presence of *Galium aparine* and *Chenopodium album* in the cultivation of a legume crop (pea). According to Campiglia et al. [42], no-tillage can lead to increased occurrence of perennial weeds, which are difficult to control. This is confirmed by our study, which recorded increased numbers of the species *Elymus repens* under the NT system when compared to CT.

From the beginnings of agriculture development, weed control has been carried out using various practices such as soil tillage or crop rotation, while, in recent years, crop protection products have also been used. Widespread application of herbicides has deeply modified agro-ecosystems, negatively affecting their species' diversity and causing herbicide resistance to occur, which has resulted in the appearance of the superweeds, that is, weeds resistant to glyphosate [43,44]. This threatens many functions of an ecosystem, which is of key importance for food production [43] because biodiversity in crop fields and their surroundings performs a number of biological functions in nutrient cycling and utilization as well as in the maintenance of balance among pathogens attacking crops [45,46]. Therefore, the aim of international policy and conducted research has become biodiversity conservation, including the species diversity of agroecosystems [47,48]. According to Clements et al. [49], if diversity increases and the number of ecological interactions also increases, weed species should be viewed as an interactive community rather than an unrelated set of targets for control. The study presented in our paper demonstrates that the NT positively influences the weed community diversity in the soybean crop relative to the CT system. However, no changes were found in the diversity of segetal flora depending on the cropping system. On the other hand, a study by Suwary et al. [50] proved that monoculture cropping contributes to decreased weed species' diversity. Under conditions of winter wheat monoculture cropping, Sekutowski and Domaradzki [51] found a greater dominance of weeds and, at the same time, lower weed species' biodiversity as a result of using tillage reductions.

Weeds occurring in a crop contribute to a change in thermal and moisture conditions in it, which, thus, creates favorable conditions for developing many fungal pathogens [52]. Unwanted vegetation can be an indirect host for pathogenic fungi and contribute to the spread of diseases and greater infection of the crop [53–55]. Our research is also a confirmation of these theories because, in the present study, an increased incidence of fungal diseases was found in the treatments with the highest number and dry weight of weeds. Given the above, it is very important to timely and effectively eliminate weeds as a potential reservoir of fungal diseases [56]. Some biologically active substances used to control weed infestation can also contribute to reducing fungal diseases of soybeans. Plenty of attention has been devoted to glyphosate, which is a commonly used active substance. It results in reduced germination of spores of the fungus Phakopsora pachyrhizi, which causes soybean rust and leads to a lower incidence of this disease in plants [57,58]. Application of lactofen and sulfentrazone promotes

increased production of phytoalexin in leaves, which is a substance that inhibits the occurrence of Sclerotinia sclerotiorum in soybeans [59,60]. It should, however, be indicated that lactofen can cause minor transient damage to the crop plant [61] through which pathogens can easily penetrate into the plant [62]. In the literature, reports can be found that, among others, glyphosate application leads to increased numbers of fungi and disturbances in soil biological balance [63]. Crops are then more susceptible to infection by diverse fungal pathogens [64].

Symptoms of many diseases caused by fungi can be observed in soybeans, but, under Polish conditions, these diseases have not been economically important so far. It is also rarely necessary to use fungicides [2]. In our study, soybeans were infected with Septoria leaf spot (*Septoria glycines*) and frogeye leaf spot (*Cercospora sojina*). Soybeans were infected to the lowest degree with Ascochyta blight (*Ascochyta* sp.). Nonetheless, Amin and Melkamu [65], Bretag et al. [66], Davidson and Kimber [67] as well as Sudheesh et al. [68] have shown that the fungus *Ascochyta* sp. can pose a major threat to legume crops and cause a significant loss in yields.

The severity of infection of plants with fungal diseases is largely associated with weather conditions in a particular growing season. Both our research and the studies of other authors reveal that years characterized by a high amount of rainfall most favor the development of fungal diseases [69,70]. In our research in thermal terms, the years 2015 and 2017 proved to be unfavorable for soybean development (Figures 1 and 2). In 2015, in the sowing month and during the initial growth of soybeans (May), when its thermal requirements are very high, a lower temperature was recorded when compared to the long-term mean. The lowest rainfall level was noted in the second growing season (2015), which significantly contributed to reduced weed infestation and decreased infection of soybeans with fungal diseases. The highest rainfall was observed in 2014 and 2017. The year 2017 was unfavorable for soybean development, predominantly in terms of rainfall distribution. There was a rainfall deficit in June (flowering stage), while, in July (flowering/seed filling) and in September, excessive rainfall was recorded. Such a distribution of precipitation favored the development of fungal diseases. In 2017, a lower temperature was observed in April than in the other years of the study when compared to the long-term mean. Due to this and because of torrential rains, sowing of soybeans was done in the first 10 days of May.

Numerous studies, including the study presented in this paper, have proven that monoculture cropping contributes to increased infection of plants with fungal diseases [17,70]. Strom et al. [71] demonstrated that long-term corn and soybean monoculture was associated with substantial changes in fungal communities and their distribution, as the number of host-specific fungal pathogens increased.

In the studies by Kraska et al. [72] and Suproniene et al. [73], tillage systems did not cause differences in infection of wheat by a complex of fungal diseases. The results of our experiment, however, show that the no-tillage system is conducive to the increased prevalence of fungal diseases of soybeans. Vrandeˇci´c et al. [74] also proved that fungal infection of soybeans is much higher under reduced tillage and no-tillage conditions compared to that found under conventional tillage. According to these authors, the amount and intensity of rainfall and temperature were the most important factors influencing the level of infection.

The research presented in this paper demonstrates that the no-tillage system and monoculture cropping promoted weed infestation and infection of soybeans with fungal diseases when compared to the conventional tillage system and crop rotation. The results on soybean yield reported in a paper by Gaw ˛eda et al. [75] proved that the seed yields in the experiment in question were much lower in the no-tillage treatment. A decreasing trend in yield in a soybean monoculture was observed as late as the fourth year of the experiment [75]. It was also found that infection of soybeans with fungal diseases was greatest in 2020. A high number of weeds and the dry weight of weeds were also recorded.

#### **5. Conclusions**

**Author Contributions:** The authors contributed to this article in the following ways: Conceptualization: D.G. Data curation, D.G. and J.Ł. Formal analysis: D.G., M.H., and U.B.-M. Investigation: D.G. and M.H. Supervision: D.G. and M.H. Visualization: D.G. and U.B.-M. Writing—original draft: D.G. and M.H. Writing—review and editing: D.G. M.H. and J.Ł. All authors have read and agreed to the published version of the manuscript.

**Funding:** The Ministry of Science and Higher Education of Poland as part of statutory activities of Department of Herbology and Plant Cultivation Techniques, University of Life Sciences in Lublin, funded this research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Heavy Metal Ion Extraction Using Organic Solvents: An Application of the Equilibrium Slope Method**

Tjoon Tow Teng, Yusri Yusup and Ling Wei Low *Universiti Sains Malaysia Malaysia*

### **1. Introduction**

120 Stoichiometry and Research – The Importance of Quantity in Biomedicine

Shehata, M.R., Shoukry, M.M., Nasr, F.M.H. and van Eldik, R. (2008) Complex-formation

Shoukry, A., Rau, T., Shoukry, M., and van Eldik, R., (1998) Kinetics and mechanisms

Shoukry, M.M., Shehata, M.R., Abdel-Razik, A., Abdel-Karim, A.T. (1999) Equilibrium

Sigel, H. and Martin, R.B. (1982) Coordinating properties of the amide bond. Stability and

Sigel, H., Massoud, S.S., Corfu, N.A. (1994) Comparison of the extent of macrochelate

Tercero-Moreno, J.M., Matilla-Hernandez, A., Gonzalez-Garcia, S. and Niclos-Gutierrez, J.

Zekany, L. and Nagypal, I. (1985) in Leggett, D.J. (Ed.), Computational methods for the determination of formation constants. Plenum Press, New York, P. 71.

in metal ion-nucleic base recognition. J. Am. Chem. Soc. 116, 2958–2971 Tauler, R. Casassas, E. and Izquierdo-Ridorsa, A. (1991) Self-modelling curve resolution in

Labilization induced by S-donor chelates. Dalton Trans 779-786.

palladium(II). J Chem Soc Dalton Trans. 3105-3112.

385-426.

Anal Chim Acta. 248, 447-458.

Drugs. Chem. Rev. 99, 2451.

and Some bioligands. Monatsh Fur Chem. 130, 409-423.

reactions of dicholoro(S-methyl-L-cysteine)palladium(II) with bio-relevant ligands.

of the ligand substitution reactions of bis(amine)(cyclobutane-1,1-dicarboxylato)

studies of mixed ligand complexes involving (1,2-diaminopropane)-Palladium(II)

structure of metal ion complexes of peptides and related ligands. Chem Rev 82,

formation in complexes of divalent metal ions with guanosine (GMP2−), inosine (IMP2−), and adenosine 5́-monophosphate (AMP2−). The crucial role of N-7 basicity

studies of spectrometric titrations of multi-equilibria systems by factor analysis.

(1996) Hydrolytic species of the ion cis-diaqua(ethylenediamine) palladium(II) complex and of cis-dichloro(ethylenediamine) palladium(II): fitting its equilibrium models in aqueous media with or without chloride ion. Inorg Chim Acta. 253, 23. Wong, E. and Giandomenico, C.M. (1999) Current Status of Platinum-Based Antitumor The separation procedure of a chemical species from a matrix is essentially based on the transportation of the solute between the two involved phases, generally an organic and an inorganic one. Specifically, solvent extraction uses the concept of unique solute distribution ratios between two immiscible solvents. However, there are several situations where solutes have been observed to completely move from the inorganic to the organic phase (Anthemidis and Ioannou, 2009).

Organic solvent extraction is the transport of solutes, e.g. heavy metal ions, from an inorganic (or aqueous) phase to an organic phase. Solvents used comprise of an extractant + diluent combination. The roles of each are as follows: 1) the extractant, as a specific metal ion extractant; 2) the diluent, as a solvent condition controller, i.e. hydrophobicity, which can affect the molecules extractability (Watson, 1999; Cox, 2004). Occasionally, a phase modifier can be added to solve the problem of emulsion formation, aside from improving the phase demarcation process in an aqueous organic system (Cox, 2008).

Solvent extraction is widely applied to processes of metal ions recovery, ranging from aqueous solutions in hydrometallurgical treatment to environmental applications. It is also considered a useful technique to increase the initial concentration of the solute, commonly used in the separation processes of analytical applications (Reddy et al., 2005).

In the biomedical field, supported liquid membrane methodology was used for trace analytes determination by facilitating chromatogram differentiation between samples, water and blood plasma (Jonsson and Mathiasson, 1999).  Most of them are, in part, made from petroleum. Recently, solvents such as vegetable oil (Venkateswaran

Heavy Metal Ion Extraction Using

and the aqueous phase (Eq. 2).

**2.2 Application of the equilibrium slope methodology**

����

ions reported in literature is presented.

extractant, *x*.

**2.2.1 Cu (II)**

Eq. (5).

where,

sequence, is listed below.

Organic Solvents: An Application of the Equilibrium Slope Method 123

The equilibrium constant, *Keq*, can be experimentally determined. In turn, *Keq* is a function of *Deq*, which is the equilibrium distribution ratio of the metal ion concentration in the organic

��� =����������

During the experiment, the ionic strength and the correlated activity coefficient, *a*, is needed to be kept constant by adding inert chemicals or substances such as salts. A plot of lg *Deq* against pHeq can be drawn to determine the valency of the extracted metal ion. This information can also be used to ensure the experiment validity by cross-checking the actual valency of the studied metal ion to the slope of the drawn plot. In addition, a plot of lg *Deq* versus lg [extractant], will reveal the value of the stoichiometric coefficient of the

In this section a separated study on the equilibrium slope method applied to different metal

Cu(II) extraction from aqueous solutions using different organic solvents have been studied extensively. Combination of extractants and solvents used to extract Cu(II) from an aqueous solution in literature include D2EHPA + soybean oil (Chang et al., 2010; Chang et al., 2011), LIX 84 + kerosene (Agrawal et al., 2008), Cyanex 921 + kerosene (Leopold et al., 2010; Mishra and Devi, 2011), and Cyanex 272 + kerosene (Mohapatra et al., 2007; Agrawal et al., 2008; St

The steps required to determine the stoichiometric coefficient of the extractant are somewhat similar between the extractant + solvent combinations. Thus, the general technique, in

The relevant reaction equation can be written as Eq. (3), where *Keq* is defined as Eq. (4) and

���

��� <sup>=</sup> [����][��]

�� ��������� � ���

�

John et al., 2010). The solvents employed are mostly non-polar in nature.

���� � ��������

������� �

��

(2)

���� (3)

[����][��]� (4)

et al., 2007; Chang et al., 2010), were exploited as alternatives to replace the commonly used petroleum-based organic solvents. Additionally, a family of "specialist" extractants, known as the organo-phosphorous compounds, are also employed as metal extractants (Sainz-Diaz et al., 1996).

Some examples of solvents applied in heavy metals extraction are:

### **2. Stoichiometry of heavy metals extracted using organic solvents: Equilibrium slope method**

The equilibrium slope method has been used by many researchers to determine the stoichiometry of various metal–organic complexes in organic solvents (Nagaosa and Yao, 1997; Wang and Nagaosa; 2001; Mansur et al., 2002; Kumar et al., 2009). Most of its applications deal with extracting metal ions from aqueous solutions. It has proven a very useful technique of its ease of use, and its accuracy when validated by other methods, such as numerical and quantitative analysis from Fourier Transform Infrared Spectroscopy (FTIR).

### **2.1 General equilibrium slope methodology**

Following this methodology, the extractant form in the organic solvent would need to be estimated first. Subsequently, the formulation of a balanced reaction equation would be derived. A general form of the reaction equation is shown below (Eq. 1). Some applied assumptions include that the solubility of the extractant and the metal-extractant complex in the aqueous phase is small, and that the extracted metal ions are not associated to one another.

$$\text{M}^{n+}\text{(aq)} + \text{xHR}\_{\text{(org)}} \xleftrightarrow{\text{K}\_{eq}} \text{MR}\_{\text{x}\_{\text{(org)}}} + \text{x} \text{H}^{+}\text{(aq)}\tag{1}$$

where,

The equilibrium constant, *Keq*, can be experimentally determined. In turn, *Keq* is a function of *Deq*, which is the equilibrium distribution ratio of the metal ion concentration in the organic and the aqueous phase (Eq. 2).

$$D\_{eq} = \left(\frac{\mathcal{M}^{n+}\!\_{\left(org\right)}}{\mathcal{M}^{n+}\!\_{\left(aq\right)}}\right)\_{eq} \tag{2}$$

During the experiment, the ionic strength and the correlated activity coefficient, *a*, is needed to be kept constant by adding inert chemicals or substances such as salts. A plot of lg *Deq* against pHeq can be drawn to determine the valency of the extracted metal ion. This information can also be used to ensure the experiment validity by cross-checking the actual valency of the studied metal ion to the slope of the drawn plot. In addition, a plot of lg *Deq* versus lg [extractant], will reveal the value of the stoichiometric coefficient of the extractant, *x*.

### **2.2 Application of the equilibrium slope methodology**

In this section a separated study on the equilibrium slope method applied to different metal ions reported in literature is presented.

### **2.2.1 Cu (II)**

122 Stoichiometry and Research – The Importance of Quantity in Biomedicine

et al., 2007; Chang et al., 2010), were exploited as alternatives to replace the commonly used petroleum-based organic solvents. Additionally, a family of "specialist" extractants, known as the organo-phosphorous compounds, are also employed as metal extractants (Sainz-Diaz

a. Di-2-ethylhexylphosphoric acid (D2EHPA), extensively used as an extractant for the separation of Cu(II) from aqueous solutions (Gherrou et al., 2002; Ren et al., 2007; Cox, 2008). Other applications of this popular extractant include the removal of Cd(II) (Kumar et al., 2009), Zn(II) (Vahidi et al., 2009), Fe(III) and Ti(IV) (Silva et al., 2008); b. Tributylphosphate (TBP), used to act as a phase modifier in Cu(II) extraction (Cheng, 2000). The extractant and phase modifier are diluted at certain ratios in the petroleumbased organic diluents such as kerosene and chloroform (Ak et al., 2008; Ren et al., 2008), cumene (Svendsen et al., 1990), dichloromethane (Memon et al., 2003), isododecane (Mortes and Bart, 2000), n-dodecane (Simonin et al., 2003), n-decanol (Lin et al., 2005), n-

heptane (Morais and Mansur, 2004), and n-hexane (Valenzuela et al., 2002);

**2. Stoichiometry of heavy metals extracted using organic solvents:**

d. Cyanex 921, used to extract Cu(II) from HCl (Mishra and Devi, 2011);

in kerosene, used to extract Cu(II) (Fu et al., 2011).

c. LIX 84 and Cyanex 272, applied to extract Cu(II), Ni(II), and Al(III) (Mohapatra et al.,

e. Miscellaneous organic solvents such as I) 1-phenyl-3-heptyl-1,3-propanedione, II) 1 phenyl-4-ethyl-1,3-octanedione, III) 1-(4'-dodecyl) phenyl-3-tert-butyl-1,3-propanedione

The equilibrium slope method has been used by many researchers to determine the stoichiometry of various metal–organic complexes in organic solvents (Nagaosa and Yao, 1997; Wang and Nagaosa; 2001; Mansur et al., 2002; Kumar et al., 2009). Most of its applications deal with extracting metal ions from aqueous solutions. It has proven a very useful technique of its ease of use, and its accuracy when validated by other methods, such as numerical and quantitative analysis from Fourier Transform Infrared Spectroscopy

Following this methodology, the extractant form in the organic solvent would need to be estimated first. Subsequently, the formulation of a balanced reaction equation would be derived. A general form of the reaction equation is shown below (Eq. 1). Some applied assumptions include that the solubility of the extractant and the metal-extractant complex in the aqueous phase is small, and that the extracted metal ions are not associated to one

ርሮ ܯܴ௫ሺሻ ܪݔା

ሺሻ (1)

ܯାሺሻ ܪݔܴሺሻ

Some examples of solvents applied in heavy metals extraction are:

et al., 1996).

2007; Agrawal et al., 2008);

**Equilibrium slope method**

**2.1 General equilibrium slope methodology**

(FTIR).

another.

where,

Cu(II) extraction from aqueous solutions using different organic solvents have been studied extensively. Combination of extractants and solvents used to extract Cu(II) from an aqueous solution in literature include D2EHPA + soybean oil (Chang et al., 2010; Chang et al., 2011), LIX 84 + kerosene (Agrawal et al., 2008), Cyanex 921 + kerosene (Leopold et al., 2010; Mishra and Devi, 2011), and Cyanex 272 + kerosene (Mohapatra et al., 2007; Agrawal et al., 2008; St John et al., 2010). The solvents employed are mostly non-polar in nature.

The steps required to determine the stoichiometric coefficient of the extractant are somewhat similar between the extractant + solvent combinations. Thus, the general technique, in sequence, is listed below.

The relevant reaction equation can be written as Eq. (3), where *Keq* is defined as Eq. (4) and Eq. (5).

$$\text{Cu}^{2+} \text{(aq)} + \text{xHR}\_{\text{(org)}} \overset{\text{K}\_{aq}}{\leftrightarrow} \text{CuR}\_{\text{x(org)}} + \text{xH}^{+} \text{(aq)}\tag{3}$$

where,

$$K\_{eq} = \frac{[\text{Cu}R\_x][H^+]^x}{[\text{Cu}^{2+}][HR]^x} \tag{4}$$

$$D\_{eq} = \left(\frac{\mathbb{C}u^{2+}\!\_{\left(org\right)}}{\mathbb{C}u^{2+}\!\_{\left(aq\right)}}\right)\_{eq} \tag{5}$$

$$K\_{eq} = D\_{eq} \frac{[H^+]^x}{[HR]^x} \tag{6}$$

$$K\_{eq} [HR]^\mathbf{x} = D\_{eq} [H^+]^\mathbf{x} \tag{7}$$

$$
\lg K\_{eq} + \varkappa \lg \text{[HR]} = \lg D\_{eq} + \varkappa \lg \text{[H}^+\text{]}\tag{8}
$$

$$
\pi \text{xlg}[H^+] = -\text{xp}H\_{eq} \tag{9}
$$

$$
\log D\_{ea} = \mathfrak{x}pH\_{ea} + \lg K\_{ea} + \mathfrak{x}\lg\{HR\} \tag{10}
$$

$$\text{Ni}^{2+}(aq) + \text{xHR}\_{(org)} \overset{\text{K}\_{aq}}{\longleftrightarrow} \text{NiR}\_{\text{x(org)}} + \text{xH}^{+}(aq) \tag{11}$$

$$K\_{eq} = \frac{[NlR\_{\overline{x}}][H^{+}]^{\overline{x}}}{[Nl^{2}"][HR]^{\overline{x}}} \tag{12}$$

$$\left(M^{n+}\right)\_{(aq)} + (n+p)/2 \overline{\left(\overline{H\_2} \overline{R\_2}\right)}\_{org} \overset{\kappa\_{eq}}{\leftrightarrow} \overline{\left(\overline{M\_n} \overline{\left(\overline{H} \overline{R\_n}\right)}\_{pg}\right)\_{org}} + nH^+\_{aq} \tag{13}$$

$$\text{Cr}^{3+}(aq) + \text{xHR}\_{\text{(org)}} \xleftrightarrow{\text{K}\_{aq}} \text{CrR}\_{\text{x(org)}} + \text{xH}^{+}(aq) \tag{14}$$

$$K\_{eq} = \frac{[CrR\_{\chi}][H^{+}]^{\text{x}}}{[Cr^{3+}][HR]^{\text{x}}} \tag{15}$$

$$D\_{eq} = \left(\frac{\mathcal{C}r^{3\*}\,\_{(org)}}{\mathcal{C}r^{3\*}\,\_{(aq)}}\right)\_{eq} \tag{16}$$

$$K\_{eq} = D\_{eq} \frac{[H^\*]^\mathbf{x}}{[HR]^\mathbf{x}} \tag{17}$$

$$K\_{eq} \lbrack HR \rbrack^\mathbf{x} = D\_{eq} \lbrack H^+ \rbrack^\mathbf{x} \tag{18}$$

$$
\lg D\_{eq} = \varkappa p H\_{eq} + \lg K\_{eq} + \varkappa \lg \text{[HR]} \tag{19}
$$

$$\text{Zn}^{2+} \text{(aq)} + 1.5 \text{(H}\_2\text{R}\_2\text{)}\_{\text{(org)}} \xrightarrow{K\_{aq}} \text{ZnR}\_2\text{(HR)}\_{\text{(org)}} + 2\text{H}^+ \text{(aq)}\tag{20}$$

$$\text{Co}^{2+}(aq) + \text{xHR}\_{\text{(org)}} \xleftrightarrow{\text{K}\_{\text{eq}}} \text{CoR}\_{\text{x(org)}} + \text{xH}^{+}(aq) \tag{21}$$

$$K\_{eq} = \frac{[CoR\_x][H^+]^\chi}{[Co^{2+}][HR]^\chi} \tag{22}$$

$$D\_{eq} = \left(\frac{\mathcal{Co}^{2+}\!\_{(org)}}{\mathcal{Co}^{2+}\!\_{(aq)}}\right)\_{eq} \tag{23}$$

$$K\_{eq}[HR]^\chi = D\_{eq}[H^+]^\chi \tag{24}$$

$$
\log D\_{eq} = \text{x}pH\_{eq} + \text{lg}K\_{eq} + \text{xlg}\{HR\} \tag{25}
$$

$$\text{Co}^{2+}(aq) + \text{HR}^{1\text{(2)}}(org) + \text{Cl}^-(aq) \leftrightarrow \text{Co}R^{1\text{(2)}}\text{Cl. 3}H\_2O\_{\text{(org)}} + \text{H}^+\text{(aq)}\tag{26}$$

$$[Al^{3+} + n[HR]\_2 \overset{K\_{eq}}{\leftrightarrow} AlH\_{2n-3}R\_{2n} + 3H^+ \tag{27}$$

$$K\_{eq} = \frac{[AlH\_{2n-3}R\_{2n}][H^+]^3}{[Al^{3+}][HR]\_2^n} \tag{28}$$

$$K\_{eq} = \frac{D\_{eq} [H^+]^3}{[HR]\_2^n} \tag{29}$$

$$D\_{eq} = \frac{[AlH\_{2n-3}R\_{2n}]}{[Al^{3+}]} \tag{30}$$

$$
\log \mathcal{D}\_{eq} = \lg K\_{eq} + n \lg \text{[}HR\text{]}\_2 - 3 \lg \text{[}H^+\text{]} \tag{31}
$$

$$\mathbf{y} = \boldsymbol{\beta}\_0 + \boldsymbol{\Sigma}\_{l=1}^k \boldsymbol{\beta}\_l \mathbf{x}\_l + \boldsymbol{\varepsilon} \tag{32}$$

Heavy Metal Ion Extraction Using

Vol.358, pp.822

Organic Solvents: An Application of the Equilibrium Slope Method 131

Djane, N.K.; Bergdahl, I.A.; Ndung's, K.; Schutz, A.; Johansson, G. & Mathiasson, L. (1997a).

Djane, N.K.; Ndung'u, K.; Malcus, F.; Johansson, G. & Mathiasson, L. (1997b). Supported

Fu, W.; Chen, Q., Hu, H., Niu, C. & Zhu, Q. (2011). Solvent extraction of copper from

Gherrou, A.; Kerdjoudj, H., Molinari, R. & Drioli, E. (2002). Removal of silver and copper

Leopold, A.A.; Coll, M.T., Fortuny, A., Rathore, N.S. & Sastre, A.M. (2010). Mathematical

Li, D.Q.; Wang, L.G. & Wang, Y.G. (2003). Synergistic extraction of Zinc(II) with mixtures of CA-100 and Cyanex 272. *Separation Science and Technology*, Vol.38, pp. 2291–2306. Lin, S.H.; Kao, H.C., Su, H.N. & Juang, R.S. (2005). Effect of formaldehyde on Cu(II) removal

Lindegrad, B.; Jonsson, J.A. & Mathiasson, L. (1992). Liquid membrane work-up of blood

Mansur, M.B.; Slater, M.J. & Biscaia Jr, E.C. (2002). Equilibrium analysis of the reactive liquid-

Memon, S.; Akceylan, E., Sap, B., Tabakci, M., Roundhill, D.M. & Yilmaz, M. (2003). Polymer

Mishra, S. & Devi, N. (2011). Extraction of copper (II) from hydrochloric acid solution by

Mohapatra, D.; Kim, H.I., Nam, C.W. & Park, K.H. (2007). Liquid-liquid extraction of

Mortes, M. & Bart, H.J. (2000). Extraction equilibria of zinc with bis(2 ethylhexyl)phosphoric

Nagaosa, Y. & Yao, B.H. (1997). Extraction equilibria of some transition metal ions by bis(2-

and D2EHPA. *Separation and Purification Technology,* Vol.56, pp. 311-318. Morais, B.S. & Mansur, M.B. (2004). Characterization of the reactive test system

ZnSO4/D2EHPA in n-heptane, *Hydrometallurgy,* Vol.74, pp. 11–18.

acid. *Journal of Chemical Engineering Data*, Vol.45, pp. 82–85.

ethylhexyl)phosphoric acid. *Talanta*, Vol.44, pp. 327-337.

*Separation and Purification Technology*, Vol.80, pp. 52-59.

*Hydrometallurgy,* Vol.96, pp. 230–234.

*Journal of Chromatography,* Vol.573, pp.191-200.

*Chemical Engineering and Processing*, Vol.45, pp. 684-690.

Cyanex 921. *Hydrometallurgy,* Vol.107, pp. 29-33.

anions. *Journal of Polymer and Environment*, Vol.11, pp. 67–69.

Vol.182, pp. 903-911.

*Materials,* Vol.120, pp. 1–3.

Supported liquid membrane enrichment combined with atomic absorption spectrometry for the determination of lead in urine. *Analyst*, Vol.122, pp.1073-1077.

liquid membrane enrichment using an organophosphorus extractant for analytical trace metal determinations in river waters. *Fresenius' Journal of Analytical Chemistry*,

ammoniacal chloride solutions by sterically hindered β-diketone extractants.

ions acidic thiourea solutions with a supported liquid membrane containing D2EHPA as carrier, *Separation and Purification Technology,* Vol.28, pp. 235–244. Jonsson, J.A. & Mathiasson, L. (1999). Liquid membrane extraction in analytical sample preparation II: Applications. *Trends in Analytical Chemistry*, Vol.18, pp. 325-334. Kumar, V.; Kumar, M., Jha, M.K., Jeong, J. & Lee, J. (2009). Solvent extraction of cadmium

from sulfate solution with di(2-ethylhexyl)phosphoric acid diluted in kerosene.

modelling of cadmium (II) solvent extraction from neutral and acidic chloride media using Cyanex 923 extractant as a metal carrier. *Journal of Hazardous Materials*,

from synthetic complexed solutions by solvent extraction. *Journal of Hazardous*

plasma samples applied to gas chromatographic determination of aliphatic amines.

liquid test system ZnSO4/D2EHPA/n-heptane. *Hydrometallurgy*, Vol.63, pp. 117-126. Mellah, A. & Benachour, D. (2006). The solvent extraction of zinc and cadmium from

phosphoric acid solution by di-2-ethylhexyl phosphoric acid in kerosene diluent.

supported calyx arene derivatives for the extraction of metals and dichromate

aluminium (III) from mixed sulphate solutions using sodium salts of Cyanex 272

### **4. Conclusion**

Liquid extraction of heavy metals is widely applied in many fields ranging from the environmental to the biomedical discipline. In the environmental field, some of the more prominent applications include: removal and recovery of heavy metals and dyes from wastewater. In the biomedical field, liquid extraction has been used in the determination of heavy metals in human waste (e.g. urine). However, trace analytes extraction is still a great challenge in the pharmaceutical and medical industry.

In this chapter, the equilibrium slope method and its utility in estimating the stoichiometry of the resulting organic complexes has been reviewed for multiple heavy metal ions. The number of protons involved generally dictates the slope of the lg *Deq* versus pH plot. A slope of 2 is common for many heavy metals, thus attributing that the general organic complexes obtained from these extraction processes are in the form of dimers.

In brief other methods, numerical and FTIR quantitative analysis have been discussed and might be helpful to further corroborate the results of the equilibrium slope method.

### **5. References**

Liquid extraction of heavy metals is widely applied in many fields ranging from the environmental to the biomedical discipline. In the environmental field, some of the more prominent applications include: removal and recovery of heavy metals and dyes from wastewater. In the biomedical field, liquid extraction has been used in the determination of heavy metals in human waste (e.g. urine). However, trace analytes extraction is still a great

In this chapter, the equilibrium slope method and its utility in estimating the stoichiometry of the resulting organic complexes has been reviewed for multiple heavy metal ions. The number of protons involved generally dictates the slope of the lg *Deq* versus pH plot. A slope of 2 is common for many heavy metals, thus attributing that the general organic complexes

In brief other methods, numerical and FTIR quantitative analysis have been discussed and

Agrawal, A.; Manoj, M.K., Kumari, S., Bagchi, D., Kumar, V. & Pandey, B.D. (2008).

Ak, M.; Taban, D. & Deligoz, H. (2008). Transition metal cations extraction by ester and

Al-Zoubi, W.; Kandil, F. & Chebani, M.K. (2011). Solvent extraction of chromium and copper

*Arabian Journal of Chemistry*, Article in Press DOI:10.1016/j.arabjc. 2011.06.023 Anthemidis, A.N. & Ioannou, K.G. (2009). Recent development in homogenous and

Baba, A.A. & Adekola, F.A. (2011). Beneficiation of a Nigerian sphalerite mineral: solvent

Chang, S.H.; Teng, T.T. & Norli, I. (2010). Extraction of Cu(II) from aqueous solutions by

Chang, S.H.; Teng, T.T. & Norli, I. (2011). Efficiency, stoichiometry and structural studies of

Cheng, C.Y. (2000). Purification of synthetic laterite leach solution by solvent extraction

Cox, M. (2004). *Solvent extraction in hydrometallurgy*, In: J. Rydberg, M. Cox, C. Musikas, G.R.

Cox, M. (2008). *Liquid–liquid extraction and liquid membranes in the perspective of the twenty-first*

using D2EHPA. *Hydrometallurgy*, Vol.56, pp. 369-386.

United States of America, pp. 457–462.

United States of America, pp. 1–19.

Extractive separation of copper and nickel from copper bleed stream by solvent

ketone derivatives of chromogenic azocalix arenes. *Journal of Hazardous Materials,*

using Schiff base derived from terephthaldialdehyde and 5-amino-2-methoxy phenol.

dispersive liquid-liquid extraction for inorganic elements determination. A review.

extraction of zinc by Cyanex 272 in hydrochloric acid. *Hydrometallurgy*, Vol. 109,

vegetable oil-based organic solvents. *Journal of Hazardous Materials,* Vol.181, pp. 868-872.

Cu(II) removal from aqueous solutions using di-2-ethylhexylphosphoric acid and tributhylphosphate diluted in soybean oil. *Chemical Engineering Journal*, Vol.166, pp.

Choppin (Eds.), Solvent Extraction Principles and Practice, Marcel Dekker Inc., The

*century*, In: M. Aguilar, J.L. Cortina (Eds.), Solvent Extraction and Liquid Membranes Fundamentals and Applications inNewMaterials, CRC Press, the

might be helpful to further corroborate the results of the equilibrium slope method.

extraction route. *Minerals Engineering*, Vol.21, pp. 1126-1130.

challenge in the pharmaceutical and medical industry.

obtained from these extraction processes are in the form of dimers.

**4. Conclusion**

**5. References**

Vol.154, pp. 51-52.

pp. 187-193.

249-255.

*Talanta,* Vol.80, pp. 413-421.

**Part 3**

**Stoichiometry in Lipids**

**and Polymers Architecture**


*Results from Telehealth DOI: http://dx.doi.org/10.5772/intechopen.101183*

#### **Figure 11.**

*Forest plot of the effect of telehealth on duration of physical activity [46].*

#### **Figure 12.**

*Forest plot of the effect of telehealth on 6 minute walk [46].*

#### **Figure 13.**

*Forest plot of the effect of telehealth on dyspnoea [46].*

telehealth have concentrated on monitoring vital signs to predict exacerbation, primarily measuring daily SpO2, and concentrate efforts on those in greatest need.

A meta-analysis of studies using vital signs to manage patients with severe COPD [47] shows that there is little change to FEV1; a primary measure of the severity of COPD. As telehealth is not a treatment, and COPD is progressive, then this might be expected. However, every exacerbation can reduce lung function, thus the earlier treatment for the exacerbation is started - the less might be the impact on lung function.