**3.1 Dry matter prediction**

The calibration and prediction model (Figure 2) statistics for each individual year (Table 1) for both harvest locations indicate that FT-NIRS in diffuse reflectance has potential as a screening tool to predict %DM on whole 'Hass' avocado fruit. The 2006 and 2007 harvest seasons had lower standard deviations (SD) than the 2008 season for both the Bundaberg and Childers locations. For the two harvest locations the 2008 harvest season calibration and prediction statistics were the best in terms of regression (R2) and SDR. The RMSEP for each harvest season varied between 1.29 to 1.49 %DM and 1.41 to 1.94 %DM for Childers and Bundaberg respectively. This suggests that the fruit obtained from the 2006 and 2007 harvest seasons possibly did not include a sufficiently broad variability in physiological attributes to develop a more suitable calibration model as seen with the 2008 harvest season, although other biological or environment effects may have contributed. The number of latent variables are within an acceptable range for the number of samples for all models (Hruschka, 1987; Lammertyn et al., 2000).

Table 1. PLS calibration (CAL) and prediction (PRE) statistics for %DM for whole 'Hass' avocado fruit harvested from Bundaberg (Bu) and Childers (Ch) over the 2006, 2007 and 2008 seasons. *Note: OR = outliers removed; LV = latent variables; n = number of samples.*

The calibration and prediction model (Figure 2) statistics for each individual year (Table 1) for both harvest locations indicate that FT-NIRS in diffuse reflectance has potential as a screening tool to predict %DM on whole 'Hass' avocado fruit. The 2006 and 2007 harvest seasons had lower standard deviations (SD) than the 2008 season for both the Bundaberg and Childers locations. For the two harvest locations the 2008 harvest season calibration and prediction statistics were the best in terms of regression (R2) and SDR. The RMSEP for each harvest season varied between 1.29 to 1.49 %DM and 1.41 to 1.94 %DM for Childers and Bundaberg respectively. This suggests that the fruit obtained from the 2006 and 2007 harvest seasons possibly did not include a sufficiently broad variability in physiological attributes to develop a more suitable calibration model as seen with the 2008 harvest season, although other biological or environment effects may have contributed. The number of latent variables are within an acceptable range for the number of samples for all models

Mean SD LV R2 RM

CAL 222(2) 18.2-35.0 27.2 3.5 7 0.75 1.76 -0.159 0.759 2.0 PRE 407 20.334.2 27.6 3.0 7 0.75 1.50 -0.582 0.818 2.0

CAL 211(0) 19.0-34.4 25.7 2.8 8 0.76 1.39 -0.0024 0.779 2.0 PRE 398(0) 19.7-32.5 25.7 2.6 8 0.70 1.41 0.112 0.754 1.8

CAL 209(1) 15.2-35.5 25.6 5.7 6 0.90 1.76 -0.0036 0.910 3.2 PRE 397(0) 15.6-35.1 25.8 5.7 6 0.88 1.94 0.1526 0.865 2.9

CAL 207 (2) 21.4-39.7 30.2 3.7 9 0.82 1.57 0.006 0.829 2.4 PRE 425 (0) 21.7-37.9 29.5 3.3 9 0.80 1.47 0.0761 0.850 2.2

CAL 209 (0) 21.9-36.8 29.1 3.3 8 0.83 1.36 -0.0098 0.842 2.4 PRE 400 (1) 22.2-36.2 29.2 3.0 8 0.81 1.29 -0.2867 0.835 2.3

CAL 209 (2) 16.1-36.2 25.6 5.2 7 0.93 1.39 0.0098 0.934 3.8 PRE 399 (0) 16.5-36.1 26.0 5.4 7 0.92 1.49 -0.1594 0.858 3.6

Table 1. PLS calibration (CAL) and prediction (PRE) statistics for %DM for whole 'Hass' avocado fruit harvested from Bundaberg (Bu) and Childers (Ch) over the 2006, 2007 and 2008 seasons. *Note: OR = outliers removed; LV = latent variables; n = number of samples.*

SECV

RM

SEP Bias Slope SDR

**3. Results and discussion**

(Hruschka, 1987; Lammertyn et al., 2000).

Bu-2006 629 18.2-35.0 27.5 3.2

Bu-2007 609 19.0-34.4 25.7 2.7

Bu-2008 606 15.2-35.5 25.7 5.7

Ch-2006 632 21.4-39.7 29.8 3.4

Ch-2007 609 21.9-36.8 29.2 3.1

Ch-2008 608 16.1-36.2 25.8 5.3

%DM range

Spectra n (OR)

Location - Year

**3.1 Dry matter prediction**

Fig. 2. Model predictions plotted against actual constituent values for %DM for (a) Bundaberg 2006 season, (b) Childers 2006 season, (c) Bundaberg 2007 season, (d) Childers 2007 season, (e) Bundaberg 2008 season, and (f) Childers 2008 season.

Large seasonal effects have a major consequence for calibration models for horticultural produce, since the spectral deviations due to biological variability of future samples cannot be predicted (Peirs et al., 2003). The influence of seasonal variability was investigated for the Bundaberg and Childers growing locations over three years. For both growing locations, the

The Application of Near Infrared Spectroscopy

14 19 24 29 34 **Reference %Dry Matter**

actual constituent values for %dry matter.

accuracy (Bobelyn et al., 2010).

Bundaberg and Childers locations collected over 3 years.

(a) (b)

**Predicted %Dry Matter**

for the Assessment of Avocado Quality Attributes 221

Fig. 3. Model prediction for the combined 2006-08 calibration model for both (a) Bundaberg and (b) Childers locations predicting on the combined 2006-08 prediction set plotted against

This study demonstrated that including data from multiple growing seasons in the calibration model will improve the predictive performance, in comparison to calibration models developed using an individual season. This is in agreement with the previous studies on this topic (Peiris et al., 1998; Peirs et al., 2003; Miyanoto and Yoshinobu, 1995; Liu et al., 2005; Guthrie et al., 2005). As more biological variability is built into the model, the prediction accuracy becomes less sensitive to unknown changes of external factors (Bobelyn et al., 2010). However, in some cases, including more biological variability (at the risk of including atypical data) in the calibration set can significantly reduce the models prediction

Geographic location (growing regions) effects may also have a major consequence on model robustness as fruit composition is subject to within tree variability (i.e., tree age, crop load, position within the tree, light effects); within orchard variability (i.e., location of tree, light effects); and intra-orchard variability (i.e., soil characteristics, nutrition, weather conditions, fruit age and season variability) (Marques et al., 2006; Peirs et al., 2003). The influence of geographic location variability on %DM for whole avocado fruit was subsequently investigated by assessing calibration model performance using avocado fruit obtained from

The PLS calibration and prediction model statistics for both the Bundaberg and Childers harvest locations and combination of both regions are presented in Table 3. The Bundaberg data set of 1844 spectra was separated into a calibration set (n = 600) and a prediction set (n = 1244). The validation statistics of the calibration model were quite good and delivered an Rv2 = 0.87 with an RMSEP = 1.48 and SDR of 2.8 for %DM. An SDR value between 2.5 and 2.9 is regarded as adequate for screening (Nicolaï et al., 2007; Schimleck et al., 2003; Williams, 2008). The Bundaberg PLS model was used to predict on the entire Childers population. As expected the application of the Bundaberg model to a population from another growing region was not as successful, providing a substantially reduced predictive performance with an Rv2 = 0.59, RMSEP = 2.84 and SDR of 1.55. Similarly, the Childers data set of 1848 spectra were separated into a calibration set (n = 624) and prediction set (n = 1224).

**Predicted %Dry M**

 **atter**

> 14 19 24 29 34 **Reference %Dry Matter**

2006 calibration model was used to predict on the 2007 season population. A combined calibration set using spectra from 2006 and 2007 seasons was used to develop a calibration model that was then subsequently used to predict the 2008 season population. A combined calibration set of 2006, 2007 and 2008 seasons was used to predict over all 3 years. Table 2 displays the summary statistics of the PLS calibration and prediction models for these combinations.

Table 2. PLS calibration (CAL) and prediction (PRE) statistics for %DM for whole 'Hass' avocado fruit from both Bundaberg (Bu) and Childers (Ch) for 2006, 2006-07 and 2006-08 seasons predicting on 2007, 2008 and 2006-08 seasons respectively. *Note: OR = outliers removed; LV = latent variables; n = number of samples.*

As expected, the application of single seasonal calibrations to populations from other growing seasons was not very successful due to seasonal biological variation. For example, the 2006 calibration models for both Bundaberg and Childers could not be used to predict the 2007 season population for the corresponding harvest location. Model predictive performance improved as more biological variability was included in the models, as seen when the combined 2006 and 2007 models was used to predict on the 2008 season. The combined 2006, 2007 and 2008 calibration models (Figure 3) was sufficiently robust to predict %DM of whole Hass avocado to within 1.48% with an Rv2 = 0.87 and SDR of 2.8 for Bundaberg; andto within 1.43 %DM with and an Rv2 = 0.89 and SDR of 3.0 for the Childers harvest location. This indicated an ability to sort the fruit into three categories with approximately 80% accuracy (Guthrie et al., 1998).

2006 calibration model was used to predict on the 2007 season population. A combined calibration set using spectra from 2006 and 2007 seasons was used to develop a calibration model that was then subsequently used to predict the 2008 season population. A combined calibration set of 2006, 2007 and 2008 seasons was used to predict over all 3 years. Table 2 displays the summary statistics of the PLS calibration and prediction models for these

SD LV R2

Bu-2007 609 14.1-34.4 2.7 0.09 5.07 4.358 0.5

Bu-2008 606 15.2-35.5 5.7 0.45 4.3 0.161 1.4

2006-08 1244(1) 14.1-35.6 4.1 6 0.87 1.48 0.0104 2.8

Ch-2007 609(0) 21.9-36.9 3.1 9 0.14 2.84 1.601 1.1

Ch-2008 608(0) 16.1-36.2 5.3 12 0.79 2.45 -0.547 2.2

2006-08 1224(0) 16.5-37.9 4.3 10 0.89 1.43 -0.021 3.0

Bu-2006 222(2) 18.2-35.0 3.5 7 0.75 1.76 -0.159 2.0

2006-07 426 14.1-35.0 3.1 9 0.75 1.60 0.112 1.9

2006-08 600(4) 15.8-35.4 4.2 6 0.86 1.55 -0.009 2.7

Ch-2006 207(2) 21.4–39.7 3.7 9 0.82 1.57 0.006 2.4

2006-07 415(1) 21.4-39.7 3.5 12 0.82 1.49 0.003 2.4

2006-08 624(1) 16.1-39.7 4.6 10 0.88 1.62 -0.001 2.8

Table 2. PLS calibration (CAL) and prediction (PRE) statistics for %DM for whole 'Hass' avocado fruit from both Bundaberg (Bu) and Childers (Ch) for 2006, 2006-07 and 2006-08 seasons predicting on 2007, 2008 and 2006-08 seasons respectively. *Note: OR = outliers*

As expected, the application of single seasonal calibrations to populations from other growing seasons was not very successful due to seasonal biological variation. For example, the 2006 calibration models for both Bundaberg and Childers could not be used to predict the 2007 season population for the corresponding harvest location. Model predictive performance improved as more biological variability was included in the models, as seen when the combined 2006 and 2007 models was used to predict on the 2008 season. The combined 2006, 2007 and 2008 calibration models (Figure 3) was sufficiently robust to predict %DM of whole Hass avocado to within 1.48% with an Rv2 = 0.87 and SDR of 2.8 for Bundaberg; andto within 1.43 %DM with and an Rv2 = 0.89 and SDR of 3.0 for the Childers harvest location. This indicated an ability to sort the fruit into three categories with

RM SECV RM

SEP Bias SDR

combinations.

Bu-

Bu-

Ch-

Ch-

CAL PRE

Bu-

Ch-

Location - Year Spectra

n (OR)

*removed; LV = latent variables; n = number of samples.*

approximately 80% accuracy (Guthrie et al., 1998).

%DM range

Fig. 3. Model prediction for the combined 2006-08 calibration model for both (a) Bundaberg and (b) Childers locations predicting on the combined 2006-08 prediction set plotted against actual constituent values for %dry matter.

This study demonstrated that including data from multiple growing seasons in the calibration model will improve the predictive performance, in comparison to calibration models developed using an individual season. This is in agreement with the previous studies on this topic (Peiris et al., 1998; Peirs et al., 2003; Miyanoto and Yoshinobu, 1995; Liu et al., 2005; Guthrie et al., 2005). As more biological variability is built into the model, the prediction accuracy becomes less sensitive to unknown changes of external factors (Bobelyn et al., 2010). However, in some cases, including more biological variability (at the risk of including atypical data) in the calibration set can significantly reduce the models prediction accuracy (Bobelyn et al., 2010).

Geographic location (growing regions) effects may also have a major consequence on model robustness as fruit composition is subject to within tree variability (i.e., tree age, crop load, position within the tree, light effects); within orchard variability (i.e., location of tree, light effects); and intra-orchard variability (i.e., soil characteristics, nutrition, weather conditions, fruit age and season variability) (Marques et al., 2006; Peirs et al., 2003). The influence of geographic location variability on %DM for whole avocado fruit was subsequently investigated by assessing calibration model performance using avocado fruit obtained from Bundaberg and Childers locations collected over 3 years.

The PLS calibration and prediction model statistics for both the Bundaberg and Childers harvest locations and combination of both regions are presented in Table 3. The Bundaberg data set of 1844 spectra was separated into a calibration set (n = 600) and a prediction set (n = 1244). The validation statistics of the calibration model were quite good and delivered an Rv2 = 0.87 with an RMSEP = 1.48 and SDR of 2.8 for %DM. An SDR value between 2.5 and 2.9 is regarded as adequate for screening (Nicolaï et al., 2007; Schimleck et al., 2003; Williams, 2008). The Bundaberg PLS model was used to predict on the entire Childers population. As expected the application of the Bundaberg model to a population from another growing region was not as successful, providing a substantially reduced predictive performance with an Rv2 = 0.59, RMSEP = 2.84 and SDR of 1.55. Similarly, the Childers data set of 1848 spectra were separated into a calibration set (n = 624) and prediction set (n = 1224).

The Application of Near Infrared Spectroscopy

**Predicted %Dry Matter**

against actual constituent values for %DM.

present here-abouts in the β vectors.

0.75) and Walsh et al. (2004) (Rc

for the Assessment of Avocado Quality Attributes 223

14 19 24 29 34

Fig. 4. Model prediction for the Bundaberg and Childers combined 2006-08 calibration model predicting on the Bundaberg and Childers combined 2006-08 prediction set plotted

in these circumstances the long-wavelength region can provide relevant information.

The regression coefficient vectors β for the dry matter calibration models across all years in this avocado study had many similar peak positions over the 850 – 2250 nm range. However as expected, there were slight differences in wavelength selection from one year to another which can be attributed to seasonal variability. Relevant spectral information for the calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 900 - 980 nm region (second and third overtone), with further contribution from absorbance bands for oil in the vicinity of 1360, 1703, 1722 and 1760 nm. However, these assignments can only be tentative because of other peaks and troughs

The results of this study are very encouraging and compare favourably to the results obtained by Clark et al. (2003) (RMSEP of 2.6 %DM over a 20 - 45 %DM range and an Rv2 of

unspecified cultivar) using a fixed PDA spectrometer in reflectance mode. The current FT-NIRS reflectance combined models for both Bundaberg and Childers compare well with the model accuracy obtained by Clark et al. (2003) (Rv2 of 0.88 and an RMSEP of 1.8 %DM) using a PDA spectrometer in interactance mode, indicating reflectance FT-NIRS may be a suitable

2 = 0.79, RMSECV = 1.14, SDR = 2.2, for %DM of an

**Reference %Dry Matter**

930 nm (overtones of CH2 stretching) (Clark et al., 2003; Guthrie et al., 2004; Osborne et al., 1993). Williams and Norris (1987) report that the 1300 - 1750 nm range is very fruitful for absorbers for use in the determination of protein and oil. The 900 - 920 nm absorbance band is often cited as the most important band for %DM and/or sugar determination, as it is removed from the troublesome interferences from the water absorbance peaks that typically dominate spectra of fruit (Clark et al., 2003). However, light penetration depth is wavelength dependent (Lammertyn et al., 2000). The 700 - 1100 nm short-wavelength NIR region allows better penetration into biological material, while wavelengths above 1100 nm (long-wavelength region) have limited penetration providing information only relatively close to the surface (Guthrie et al., 2004; Saranwong & Kawano, 2007). In some instances, there may be secondary correlations between skin properties and those of the bulk flesh and

Table 3. PLS calibration and prediction statistics for %DM for whole 'Hass' avocado fruit harvested over three seasons for Bundaberg and Childers growing locations and combination of both regions. *Note: OR = outliers removed; LV = latent variables; n = number of samples.*

The Childers PLS model also produced reasonable validation statistics (Rv2 = 0.89 with an RMSEP = 1.43 and SDR of 3.0) when predicting fruit from within the Childers region. As with the Bundaberg model, the Childers model did not perform as well when it was used to predict %DM of fruit from a different geographic location such as the combined 2006-08 Bundaberg population (Rv2 = 0.74 with an RMSEP = 2.14 and SDR of 1.96).

A calibration model was developed by combining both Bundaberg and Childers populations, incorporating biological variability from both regions over three growing seasons. Model predictive performance of the combined population was comparable to the individual regional models of Bundaberg and Childers, with an Rv2 = 0.89, RMSEP = 1.42, and SDR of 3.1 (Figure 4). These results demonstrate that there are spectral differences between growing districts and that each individual regional model does not incorporate the relevant spectral information enabling the model to successfully predict samples containing biological variability from a different growing district without reduced predictive performance. It is therefore important that calibrations be developed on populations representative in which sorting is to be attempted.

Interpreting NIR models in terms of the various fruit components is often difficult due to spectral co-linearity where information in a model may not necessarily be carried by just a few independent wavelengths, but is possibly a combined effect of many wavelengths with each contributing only relatively little information (McGlone & Kawano, 1998). For oil, strong electromagnetic absorption is reported around 2200 – 2400 nm (CH2 stretch bend and combinations), with weaker absorption around 1750, 1200 and 900 – 920 nm ranges, and

Range SD LV R2 RM

SECV

RM SEP SDR

%DM

Bundaberg 600(4) 15.8-35.4 4.2 6 0.86 1.55 2.7

Bundaberg 1244(1) 14.1-35.6 4.1 6 0.87 1.48 2.8

Childers 1847 16.1-39.7 4.4 6 0.59 2.84 1.55

Childers 624(1) 16.1-39.7 4.6 10 0.88 1.62 2.8

Childers 1224(0) 16.5-37.9 4.3 10 0.89 1.43 3.0

Bundaberg 1844(1) 14.1-35.5 4.2 10 0.74 2.14 1.96

Childers 1224(4) 15.8-39.7 4.5 9 0.88 1.55 2.9

Table 3. PLS calibration and prediction statistics for %DM for whole 'Hass' avocado fruit

combination of both regions. *Note: OR = outliers removed; LV = latent variables; n = number of*

The Childers PLS model also produced reasonable validation statistics (Rv2 = 0.89 with an RMSEP = 1.43 and SDR of 3.0) when predicting fruit from within the Childers region. As with the Bundaberg model, the Childers model did not perform as well when it was used to predict %DM of fruit from a different geographic location such as the combined 2006-08

A calibration model was developed by combining both Bundaberg and Childers populations, incorporating biological variability from both regions over three growing seasons. Model predictive performance of the combined population was comparable to the individual regional models of Bundaberg and Childers, with an Rv2 = 0.89, RMSEP = 1.42, and SDR of 3.1 (Figure 4). These results demonstrate that there are spectral differences between growing districts and that each individual regional model does not incorporate the relevant spectral information enabling the model to successfully predict samples containing biological variability from a different growing district without reduced predictive performance. It is therefore important that calibrations be developed on populations

Interpreting NIR models in terms of the various fruit components is often difficult due to spectral co-linearity where information in a model may not necessarily be carried by just a few independent wavelengths, but is possibly a combined effect of many wavelengths with each contributing only relatively little information (McGlone & Kawano, 1998). For oil, strong electromagnetic absorption is reported around 2200 – 2400 nm (CH2 stretch bend and combinations), with weaker absorption around 1750, 1200 and 900 – 920 nm ranges, and

harvested over three seasons for Bundaberg and Childers growing locations and

Bundaberg population (Rv2 = 0.74 with an RMSEP = 2.14 and SDR of 1.96).

Childers 2468(1) 14.1-37.9 4.3 9 0.89 1.42 3.1

Harvest Location Spectra n

Bundaberg &

representative in which sorting is to be attempted.

Calibration Prediction

Bundaberg &

*samples.*

(OR)

Fig. 4. Model prediction for the Bundaberg and Childers combined 2006-08 calibration model predicting on the Bundaberg and Childers combined 2006-08 prediction set plotted against actual constituent values for %DM.

930 nm (overtones of CH2 stretching) (Clark et al., 2003; Guthrie et al., 2004; Osborne et al., 1993). Williams and Norris (1987) report that the 1300 - 1750 nm range is very fruitful for absorbers for use in the determination of protein and oil. The 900 - 920 nm absorbance band is often cited as the most important band for %DM and/or sugar determination, as it is removed from the troublesome interferences from the water absorbance peaks that typically dominate spectra of fruit (Clark et al., 2003). However, light penetration depth is wavelength dependent (Lammertyn et al., 2000). The 700 - 1100 nm short-wavelength NIR region allows better penetration into biological material, while wavelengths above 1100 nm (long-wavelength region) have limited penetration providing information only relatively close to the surface (Guthrie et al., 2004; Saranwong & Kawano, 2007). In some instances, there may be secondary correlations between skin properties and those of the bulk flesh and in these circumstances the long-wavelength region can provide relevant information.

The regression coefficient vectors β for the dry matter calibration models across all years in this avocado study had many similar peak positions over the 850 – 2250 nm range. However as expected, there were slight differences in wavelength selection from one year to another which can be attributed to seasonal variability. Relevant spectral information for the calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 900 - 980 nm region (second and third overtone), with further contribution from absorbance bands for oil in the vicinity of 1360, 1703, 1722 and 1760 nm. However, these assignments can only be tentative because of other peaks and troughs present here-abouts in the β vectors.

The results of this study are very encouraging and compare favourably to the results obtained by Clark et al. (2003) (RMSEP of 2.6 %DM over a 20 - 45 %DM range and an Rv2 of 0.75) and Walsh et al. (2004) (Rc 2 = 0.79, RMSECV = 1.14, SDR = 2.2, for %DM of an unspecified cultivar) using a fixed PDA spectrometer in reflectance mode. The current FT-NIRS reflectance combined models for both Bundaberg and Childers compare well with the model accuracy obtained by Clark et al. (2003) (Rv2 of 0.88 and an RMSEP of 1.8 %DM) using a PDA spectrometer in interactance mode, indicating reflectance FT-NIRS may be a suitable

The Application of Near Infrared Spectroscopy

**Time after impact (hours)**  **Spectra (n)**

1-2 102 (i) 0 - 10;

'Hass' avocado fruit. *Note: LV = latent variables; n = number of samples.*

developed on populations representative in which sorting is to be attempted.

enable the development of a robust model suitable for commercial use.

**Item assessed**

%Bruising

**4. Conclusion**

of scanned area

for the Assessment of Avocado Quality Attributes 225

**Defined classification (%)**

(ii) 11 - 100

Table 5. Classification statistics for prediction of percentage bruise development in whole

NIRS has come to be extensively used in many applications for the non-invasive rapid assessment of a wide variety of products. These both include quantitative compositional determinations and qualitative determinations. The present study indicates the potential of FT-NIRS in diffuse reflectance mode to be used as a non-invasive method to predict the %DM of whole 'Hass' avocado fruit and the importance of incorporating seasonal and geographical variation in the calibration model. The results showed that the calibration model robustness increased when data from more than one season, incorporating a greater range of seasonal variation, was included in the calibration set. Also, that there are spectral differences between geographical regions and that, specific regional models may have significantly reduced predictive performance when applied to samples containing biological variability from a different growing region. It is therefore important that calibrations be

As shown, there is also great potential to use FT-NIRS as a tool to predict impact damage of whole avocados based on percentage bruise development, and to predict shelf-life based on rot development (susceptibility). It should be considered that the preliminary work presented here is a first step towards shelf-life prediction and bruise detection for avocado fruit. However, this was only a preliminary study and the classification models require many more samples, incorporating seasonal and geographical biological variations, to

Overall, FT-NIR reflectance spectroscopy shows promise for the application in a commercial, in-line setting for the non-destructive evaluation of %DM, bruises and rot susceptibility of whole avocado fruit, although optimisation of the technology is required to address speed of throughput and environmental issues. Incorporating fruit physiological variability over future seasons and growing regions will be essential to further increase model robustness and ensure the predictive performance suitable for commercial use.

Unfortunately, the process of calibration development is a major impediment to the rapid adoption of NIRS in industry. The collection and precise analysis of the reference samples remains a time-consuming and a potentially costly exercise depending on the type of analysis. With this said, NIRS has an obvious place in agriculture and environmental applications with its core strength in the analysis of biological materials, plus low cost of

24 102 8 4.9 (n=5) 95.1

**LV Spectra**

**misclassified (n)**

10 9.8 (n=10) 90.2

**Spectra correctly classified (%)**

(n=92)

(n=97)

alternative for in-line and at-line environments. Another comparative study was conducted by Schmilovitch et al. (2001) for two relatively thin skin cultivars, 'Ettinger' and 'Fuerte', during a single season. They used a dispersive NIR spectrophotometer in reflectance mode in the 1200 - 2400 nm range, reporting errors of prediction for 'Ettinger' and 'Fuerte' of 0.9% and 1.3% respectively, for fruit having a 14 – 24 %DM range. It is likely that the relatively smooth to medium textured, thin-skin cultivars would not suffer to the same extent from the physiological limitations experienced in the thick rough skin of 'Hass', and prediction errors would certainly be expected to be lower. We must emphasize however, it is difficult to make a meaningful comparison of the various techniques as there is insufficient detail presented in these papers to establish if the differences are associated with the spectroscopic technique or with the geometry of the configurations used.

#### **3.2 Impact and rot assessment**

Classification statistics for the prediction of percentage rot development are presented in Table 4. The preliminary study found that by applying discriminative analysis techniques, 92.8% of the test population could be correctly classified into 2 categories, above and below 30% rot development for the area scanned. The percentage correctly classified decreased slightly to 86.8% when the classification was reduced to above and below 10% rot development for the scanned area.

Table 4. Classification statistics for prediction of percentage rot development (shelf life) of whole 'Hass' avocado fruit. *Note: LV = latent variables; n = number of samples.*

Table 5 depicts the classification statistics for the prediction of percentage bruise development. The results indicate that 90% of the population could be correctly classified into 2 categories based on percentage bruise development in the scanned area (≤10%, ≥11%) using scans conducted 1 - 2 hours following impact. Of the 10 (9.8%) samples misclassified, 6 (5.9%) samples visually rated with bruising greater than 11% were placed into the <10% bruise category and 4 (3.9%) samples with bruising visually rated below 10% were placed into the ≥11% bruise category. The 4 samples misclassified with bruising below 10% were all on the ambiguous change over point of the two defined classification categories at 10% bruising.

These results improved to >95% correctly classified when the fruit were rescanned after 24 hours following impact. It appears the 24 hour time delay allowed more time for the bruising to develop assisting with classification. This would indicate that in a commercial situation it would be an advantage to hold the fruit for 24 hours prior to scanning. The 5 (4.9%) samples misclassified were all samples with bruising visually rated below 10% and placed into the ≥11% bruise category. Of these samples 4 (3.9%) were at the ambiguous change over point of the two defined classification categories at 10% bruising.

Table 5. Classification statistics for prediction of percentage bruise development in whole 'Hass' avocado fruit. *Note: LV = latent variables; n = number of samples.*


*4.5. Wound Breaking Strength*

Wounded skin tissue was harvested from WT and *IFN-*γKO mice on Day 14 after wound creation. A strip of this tissue located 5 mm from the center of the wound was excised with a no. 15 surgical blade (Feather Safety Razor Co., Ltd., Osaka, Japan). Wound breaking strength was measured using an IMS-001 (Keisei Medical Industrial Co., Ltd., Tokyo, Japan) as previously described [38]. Briefly, each side of the strip was pinched in a clip and the two clips were pulled apart at a constant speed of 3 cm/min until the strip broke. The result was expressed as the tensile force necessary to break the repaired wounds. Tensile force is characterized as tissue fragility and delay of collagen synthesis.


*Edited by Adilson Marques,*

*Margarida Gaspar de Matos*

*and Hugo Sarmento*

Worldwide, there is a high prevalence of mental health problems such as depression and anxiety. Mental health problems can affect all people regardless of their sociodemographic characteristics. This means that anyone is likely to have some form of mental health problem. The effects of mental health problems can be so devastating that they lead to suicide in many cases. For this reason, preventive measures to avoid mental health problems are important. Among the strategies used for the prevention and treatment of mental health problems, the practice of physical activity, good levels of physical fitness, quality leisure time, socialization (especially in older adults), the use of counseling whenever there is a change in mental health, religiosity and contemplation of the divine, psychological support, and professional guidance stand out. This book presents different strategies that can be used to promote good mental health.

ISBN 978-1-80355-924-7

Mental Health - Preventive Strategies

Published in London, UK © 2023 IntechOpen © whyframestudio / iStock


**Table 3.**

*Effect of biochar on physical, chemical and biological properties of soil.*

internal structure of biochar increases soil porosity which helps to increase the surface area of soil so that water is better able to penetrate. Previous studies showed that application of biochar to infertile soils decreases soil bulk density, increases total pore volume and water holding capacity [7, 40]. Chen et al. [7] reported that biochar application decreased the tensile strength of soil cores, indicating that the use of biochar can reduce the risk of soil compaction.

## *3.3.2 Soil chemical properties*

Biochar has potential benefits in improving the chemical properties of soils. Application of biochar to soil may improve nutrient supply to plants. Soil reaction (pH) is an important characteristic of soils in terms of nutrient availability and plant growth. Previous studies reported that soil pH was raised by high-pH biochar at about one-third the rate of lime resulting in increased calcium levels and reduced aluminum toxicity on red ferralitic soils [19, 20, 41]. Soil with a high CEC helps to hold or bind plant nutrient cations to the surface of biochar particles, humus and clay, so nutrients are retained rather than leached and therefore more available for uptake by plants [19, 20, 42]. Biochars derived from manure and animal-product feedstock are relatively rich in nutrients when compared with those derived from plant materials and especially those derived from wood [43, 44]. However, biochars in general may be more important for use as a soil amendment and driver of nutrient transformation than as a primary source of nutrients [45].


**1. Introduction**

Environmental conditions are rarely ideal and plants are constantly exposed to various types of stress during their life cycle. Stress can be defined as a factor that decreases the rate of physiological processes that negatively affects growth, development and plant productivity [1]. In the context of energy consumption, stress can be observed as a state in which reduced energy production is directed towards stress-defense processes rather than growth and development [2]. In natural conditions, plants are mainly exposed to a combination of different stress-inducing factors that interact with each other and modify their individual effects accordingly. Salt stress is one of the major abiotic factors limiting crop productivity. According to Shrivastava and Kumar [3], more than 50% of lands are affected by salinity, while salinized areas have a tendency to increase by 10% every year. Since almost all food originates from soil, it is more than clear what problem salinization presents to the food supply [4]. In addition to natural salinization, which is the accumulation of dissolved salts in the soil to the levels that interfere with agricultural production and environment, there is also secondary salinization that occurs as a result of anthropogenic influences [5].

**Citation:** Trifunovi´c-Momˇcilov, M.; Stamenkovi´c, N.; Ðuri´c, M.; Miloševi´c, S.; Markovi´c, M.; Giba, Z.; Suboti´c, A. Role of Sodium Nitroprusside on Potential Mitigation of Salt Stress in Centaury (*Centaurium erythraea* Rafn) Shoots Grown In Vitro. *Life* **2023**, *13*, 154. https://doi.org/ 10.3390/life13010154

Academic Editors: Wajid Zaman and Hakim Manghwar

Received: 6 December 2022 Revised: 28 December 2022 Accepted: 3 January 2023 Published: 5 January 2023

**Copyright:** © 2023 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 (https:// creativecommons.org/licenses/by/ 4.0/).

Salt stress disrupts plant homeostasis in two ways. First, high concentrations of salt in the soil prevent water uptake by the roots, while the accumulation of salts in plants, primarily Na+ and Cl− ions, further leads to toxic effects [6]. During the initial phase of defense against salt stress, water deficit and osmotic stress causes a decrease in cell division rate in leaves, root and shoot meristems [7]. Osmotic stress also leads to stomatal closure and reduction in photosynthesis efficiency [8]. The next phase of the plant's defense against salt stress occurs due to accumulation of toxic ions, leading to damage of cell membranes' structure and function, inhibition of enzyme activity and finally, plant productivity [9]. Secondary oxidative stress follows immediately after primary stressors, osmotic stress and ion toxicity. Oxidative stress is a complex chemical and physiological phenomenon that occurs as a result of intensive production and accumulation of reactive oxygen species (ROS) which, due to high reactivity, damage proteins, lipids and nucleic acids [10]. By damaging the lipids, the integrity and functions of membranes deteriorate. These fragmentation products can further damage proteins and nucleic acids, thereby interfering with the normal functioning of receptors, enzymes and membrane channels, resulting in cell death [11]. Accordingly, lipid oxidation, also known as lipid peroxidation, is one of the markers of oxidative stress.

Since prolonged exposure to stress leads to cell death, plants have developed numerous mechanisms that enable growth in different stress conditions. Tolerance to salt stress is a complex phenomenon involving numerous regulatory processes such as stomatal opening, changes in hormonal balance, activation of antioxidant defense systems, osmotic adjustment, maintenance of water balance, export of toxic ions or their compartmentalization in vacuoles [12]. Antioxidant defense mechanisms are divided into two groups, non-enzymatic and enzymatic. Both groups of antioxidants are involved in protecting cellular components from oxidation as well as conversion of ROS into less reactive forms. In addition to ascorbic acid, glutathione, tocopherols, polyamines and phenols, proline and glycine betaine are the most important non-enzymatic components [9]. Proline is one of the essential amino acids, with great importance in protein synthesis. The accumulation of proline in plant cell results after different disturbances to the external environment [13]. In addition, proline is known to regulate the expression of genes important for mitochondrial stability, cell division and cell death [9,14,15]. Several different enzymes such as superoxide dismutase (SOD), catalase (CAT), peroxidase (POX), glutathione peroxidase (GPX), glutathione reductase (GR), glutathione S-transferases (GST), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase (DHAR), act as part of the plant antioxidant defense system [16]. In addition, SOD is considered one of the major enzymatic systems that scavenges stress-generated free radicals in plants while other enzymes such as CAT and POX, work in close synchrony with SOD to prevent the formation of more harmful ROS through the Haber–Weiss reaction.

Nitric oxide (NO) is a paramagnetic molecule with an unpaired π∗ electron, which can easily diffuse through membranes [17]. Initially, NO was considered an air pollutant that inhibits plant growth and denatures DNA, damages lipids, and decreases intensity of photosynthesis and respiration [18]. Today, however, it is known that NO is an important molecule in redox signaling, participates in the control of numerous physiological processes and plays an important role in establishing resistance to pathogens and regulating the plant's response to abiotic stress [19–21]. Sodium nitroprusside (SNP), a common NO donor, plays diverse roles in plant growth and development. Numerous studies have confirmed the protective role of SNP during salt stress conditions in tomato [22], cucumber [23], orange [24], cotton [25], alfalfa [26], apple [27], wheat [28] and lentil [29] plants.

Centaury (*Centaurium erythraea* Rafn) is medicinal plant that is widely used in traditional medicine as an antidiabetic, antipyretic, antiflatulent and detoxifying agent [30]. Various bioactive compounds isolated from the aerial part of centaury have shown different therapeutic properties [30–36]. Among the species belonging to the *Centaurium* genus, centaury is the plant species to which the greatest attention has been paid during recent years. The first and most important reason is the relatively easy manipulation of this plant species, which makes it an excellent model system for studying genetic transformation, secondary metabolites and salt stress physiology [37–40]. Moreover, centaury has recently shown to possess great developmental plasticity and the ability to induce somatic embryogenesis in root and leaf cultures [41]. A previous report described the salinity-stress response of centaury shoots and roots grown in vitro [38]. In this work, we investigated whether exogenous application of SNP can alleviate the effects of stress caused by NaCl in centaury shoots grown in vitro.

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

#### *2.1. Plant Material, Culture Conditions and Experimental Design*

Mother stock cultures of centaury plants were used as the primary plant material. The centaury shoots were cultured in vitro, on half-strength MS medium ( 1 2MS, [42]) solidified with 0.7% agar and supplemented with 3% sucrose as well as 100 mg L−1 *myo*-inositol. The medium was adjusted to pH 5.8 with NaOH/HCl and autoclaved at 121 ◦C for 25 min. All in vitro cultures were grown at 25 ± 2 ◦C and a 16/8 h light/dark photoperiod ("Tesla" white fluorescent lamps, 65 W, 4500 K; light flux of 47 μmol s−1 m−2). During the threeweek long pretreatment, the centaury shoots were first placed on four types of 1 2MS nutrient media containing different SNP concentrations (0, 50, 100 or 250 μM). After pretreatment, the centaury shoots were transferred to fresh 1 2MS nutrient media supplemented with NaCl (0 or 150 mM) and/or SNP (0, 50, 100 or 250 μM) and cultured for one week (Figure 1). All experiments were repeated three times.

**Figure 1.** Schematic of experimental design including different SNP pretreatments and NaCl and/or SNP treatments.

#### *2.2. Quantification of Photosynthetic Pigments*

Isolation of total chlorophyll (*Chl*) and carotenoids was accomplished from the leaves collected from the bottom part of the centaury rosette after four weeks of cultivation. Total *Chl* and carotenoid content were extracted using 96% ethanol as proposed by Lichtenthaler [43] and previously described in detail by Trifunovi´c-Momˇcilov et al. [39]. The absorbance of the photosynthetic pigments was measured using a UV–visible spectrophotometer (Agilent 8453, Life Sciences, Santa Clara, CA, USA).

#### *2.3. Estimation of Oxidative Stress Biomarkers*

The level of lipid peroxidation was measured as malondialdehyde (MDA) concentration by the procedure described by Heath and Packer [44], while H2O2 concentration was determined as described by Velikova et al. [45]. In both assays, 0.1% trichloroacetic acid was used and detailed protocols were previously described by Trifunovi´c-Momˇcilov et al. [38]. The spectrophotometric determination of MDA and H2O2 were measured using an ELISA Micro Plate Reader (LKB 5060–006, Winooski, VT, USA).

#### *2.4. Estimation of Nonenzymatic Antioxidants*

Free proline content was determined by the ninhydrin reaction which consists of the reaction of proline and ninhydrin reagent (2,2-dihydroxyindane-1,3-dione) resulting in a yellow reaction product [46]. Proline extraction and measurement was performed according to a modified method by Carillo and Gibon [47] and described in detail by Trifunovi´c-Momˇcilov et al. [38].

Total polyphenol content was determined using the Folin–Ciocalteu test (FC test) based on reaction of polyphenols from plant tissues and Folin–Ciocalteu reagents forming a blue-colored complex that can be spectrophotometrically quantified. This method was previously described by Singleton et al. [48]. The plant material (200 mg) was homogenized in liquid nitrogen and extracted with 96% ethanol. The homogenate was incubated for 60 min at room temperature and then centrifuged for 15 min. The supernatant was further mixed with the FC reagent solution, which was previously prepared by adding distilled water to the FC reagents in a volume ratio of 2:1. The reaction mixture was quickly vortexed and 20% Na2CO3 was added. After 90 min at room temperature in darkness, the absorbance was measured at 765 nm. In this assay, gallic acid was used as a phenol standard.

The antioxidant activity in the centaury shoots was determined after evaluation of stable DPPH radical concentrations. The samples were prepared using the same method as the FC test. In the reaction with antioxidants, the DPPH radical is converted to a non-radical form through reduction by hydrogen ions. After homogenization and centrifugation of the supernatant, methanol and DPPH reagent solution were added. The reaction mixture was incubated at room temperature in the dark. After 60 min, the degree of reduction of the DPPH radical was estimated through an absorbance measurement at 520 nm. The scavenging capacity of the DPPH radical was calculated using the following equation: (%) = [1 − (A1 − A0)] × 100 where A1 is the absorbance of the sample and A0 is the absorbance of the blank reaction.

For the spectrophotometric determination of all nonenzymatic antioxidants, an ELISA Micro Plate Reader (LKB 5060–006, Winooski, VT, USA) was used.

#### *2.5. Estimation of Enzymatic Antioxidants*

Centaury shoots were homogenized in potassium phosphate extraction buffer containing insoluble polyvinylpolypyrrolidone, dithiothreitol and phenyl methyl sulfonyl fluoride. The homogenate was centrifuged at 4 ◦C for 5 min and the protein content was determined from the supernatant according to Bradford [49] using bovine serum albumin as the standard. The quantification of SOD, CAT and POX was also performed.

SOD activity was determined spectrophotometrically using a modified method from Beyer and Fridowich [50]. The reaction mixture contained potassium phosphate buffer, ethylenediaminetetraacetic acid, methionine, nitroblue tetrazolium chloride (NBT) and riboflavin. The reaction mixtures were added to the samples, which were then illuminated for 1–2 min and the absorbance was measured at 540 nm. One unit of SOD activity is the amount of sample required for 50% inhibition of NBT photoreduction and is presented as the specific activity (U/mg). SOD activity was spectrophotometrically detected using an ELISA Micro Plate Reader (LKB 5060–006, Winooski, VT, USA).

CAT activity was determined spectrophotometrically using the method from Aebi [51]. This method is based on monitoring the kinetics of the consumption of H2O2, which can be detected by measuring the absorbance (at 240 nm) of the reaction mixture consisting

potassium phosphate buffer, H2O2 and enzyme extract. One unit of CAT activity is defined as the amount of enzyme required to degrade 1 μM of H2O2 in 1 min and is indicated as μM min−1 mg protein−1 (U/mg protein).

POX activity was determined spectrophotometrically using the method from Kukavica and Veljovi´c-Jovanovi´c [52]. The reaction mixture contained potassium phosphate buffer and pyrogallol as the enzyme substrate. The POX-catalyzed oxidation of pyrogallol to purpurogallin in the presence of H2O2 was monitored by absorbance determination at 430 nm. Enzyme activity is indicated as μM min−1 mg protein−1 (U/mg protein). The absorbances of the CAT and POX reactions were measured with a UV–visible spectrophotometer (Agilent 8453, Life Sciences, USA).

### *2.6. Statistical Analysis*

The effect of different SNP pretreatments/treatments on the biochemical parameters of centaury shoots, after four weeks of culture, were evaluated using standard two-factor analysis of variance (ANOVA). All analysed parameters were measured using three biological samples per treatment. In addition, the absorbances of all supernatants were measured in triplicate for each sample. The results are presented as mean ± SE. The comparisons between the mean values were made using a Fisher LSD (the least significant difference) post-hoc test, calculated at a confidence level of *p* ≤ 0.05.


**2. Buildings rigidly connected together long after completion without being designed as such**

A need to connect two adjacent buildings together by a bridge or corridor arises sometimes long after the buildings are constructed and occupied. Fast and easy circulation of occupants and goods is a clear benefit of linking the buildings. There are uncountable examples of buildings linked together by a bridge for similar purposes. **Figure 1** shows two hospital buildings connected by a steel sky bridge over a street with a span of around 22 m. The steel bridge is partly supported by the stone walls,

*Some Risky Practices in Earthquake Engineering That Need More Research and Evaluation DOI: http://dx.doi.org/10.5772/intechopen.108445*

#### **Figure 1.**

*Steel sky-bridge connecting two parts of a multistory hospital building in Amman, Jordan.*

and partly fixed to the walls using steel plates and bolts, **Figure 2**. The sky bridge was erected long after the two buildings were constructed when the need to use the two buildings as one hospital came into existence. Often, in such cases, a thorough seismic structural investigation of the linked buildings is not carried out.

The connecting bridges are usually built of reinforced concrete or steel. The link may be supported directly on the beams' or slabs' tops of the two buildings. The concrete of the beam or slab tops may be removed and the steel bars exposed in order to splice or weld the steel bars of the linking bridge to the steel of the beams or slabs of the buildings. Steel sections (for example, Section I) may protrude into the beam or slab concrete, or be connected by plates and bolts. In all such cases of connections, the linking bridge becomes rigidly connected to the two buildings. A new complex building is, therefore, created, which comprises the two buildings and the linking bridge. The stiffness, mass, and damping matrices of this complex replace the individual matrices of the two buildings during analysis. Seismic analysis of this complex proves that its structural behavior is completely different from that of the isolated individual buildings.

In order to obtain some insight into this case, a seismic analysis of two buildings connected by sky bridges is carried out by the author. The two buildings are 18 and 13 floors in height, with different plan configurations. Sky bridges are modeled as beam columns, and hence it is a rigid connection. The buildings are connected together by

**Figure 2.** *The connecting steel sky bridge from below.*

one, two or three sky bridges, and seismic investigations are carried out for different cases. El Centro earthquake time- acceleration recording is utilized [15]. SAP2000 analysis results revealed that the modes of vibration of the connected buildings are quite different from those of the individual buildings. The following **Figures 3**–**5** are the results of SAP200 analysis of two linked buildings. One of the outputs of the analysis shows that the modes of vibration are out of phase, **Figure 3**, and they depart from each other [15]. For example, in the structure with one sky-bridge, horizontal displacements in the right building frame U1 in **Figure 3** for the two

#### **Figure 3.**

*Out-of-phase mode of vibration and deformation of two connected buildings, a case of 1 sky bridge, with permission of the author [15].*


**1. Introduction**

The oral microbiota colonizes oral biofilm on the surface of the tooth or on the mucosa within the mouth. The accumulation of oral biofilm can lead to oral diseases such as dental caries or periodontitis [1]. Oral biofilm can lead to dental caries in two ways. First, bacteria within the oral biofilm produce acid through sugar metabolism. Second, the acid causes a subsequent decrease in the environmental pH value. Both of the above are responsible for the demineralization of the tooth surface and the formation of dental caries [2]. The amount of tooth mineral and other calcium phosphates in the plaque fluid decreases rapidly after exposure to fermentable carbohydrates. Lactic acid production and a reduction in the plaque fluid volume can result in the formation of caries [3].

Acid-base-producing bacteria, in varying numbers and proportions, are the key pathogens associated with dental caries [4]. Among the suspected acid-producing bacteria, *Mutans streptococci* (MS) has been confirmed, in a systematic literature review, to play a central role in the initiation of dental caries on both enamel and root surfaces [5]. This is the case for several reasons: first, MS is the most frequently isolated species from a caries

**Citation:** Zhang, J.; Got, S.-R.; Yin, I.X.; Lo, E.C.-M.; Chu, C.-H. A Concise Review of Silver Diamine Fluoride on Oral Biofilm. *Appl. Sci.* **2021**, *11*, 3232. https://doi.org/ 10.3390/app11073232

Academic Editor: Yoshiaki Nomura

Received: 26 February 2021 Accepted: 30 March 2021 Published: 4 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

lesion; second, MS is highly acidogenic and aciduric [6,7]; and third, MS can produce surface antigens I/II and water-insoluble glucan, which promote bacterial adhesion to the tooth surface and to other bacteria [6]. For the purpose of halting the closed circle of the process, an intervention that can inhibit the bacteria that produce acid is needed.

Silver diamine fluoride (SDF) has been widely used to prevent and arrest caries, and "silver diamine fluoride" is the most common spelling/keyword for this compound in the dental literature. SDF was approved for use as a therapeutic agent in Japan over 50 years ago [8]. It has also been used for dental caries treatment in Australia and in some countries in Latin America such as Argentina and Brazil for many years [9]. The Food and Drug Administration approved the marketing of SDF in the United States for the treatment of human dentinal hypersensitivity in adults in 2014 [10].

SDF contains silver and fluoride, which form a complex with ammonia in a colorless alkaline solution [9]. It is not only a combination of silver salt, ammonium, and fluoride ions but also a mixed heavy-metal halide co-ordination complex. Ammonia can provide an alkaline environment, thus keeping the solution constant for a certain period of time [11]. In addition, silver compounds have been reported to have antimicrobial properties in the application of medicine and dentistry for decades [12]. Fluoride is also used in various forms to prevent and arrest caries due to its remineralization function [9]. Therefore, SDF has been hypothesized to contain the combined effects of silver and fluorides.

Fluoride can inhibit the production and metabolic activity of certain strains of bacteria within biofilm at low concentrations [13]. At high concentrations, fluoride inhibits the growth of cariogenic bacteria in dental plaque [14]. One mechanism of biofilm inhibition is to bind its ions to the bacterial cell constituents and to influence enzymes such as enolase and proton-extruding adenosine triphosphatase [15]. Another mechanism is the inhibition of the carbohydrate metabolism of acidogenic oral bacteria [16,17]. However, this may contribute to the development of bacterial tolerance and shorten the duration of the antibacterial effect [18]. Nonetheless, the dominant advantage of fluoride is that the re-mineralizing effect, particularly for the SDF solution, effectively arrests dental caries [19].

Several reviews have been conducted regarding the clinical use of SDF for preventing and arresting dental caries with significant effect [20,21]. However, the rationale behind the impact of SDF on biofilm remains unclear. The aim of this systematic review was to identify the effect of SDF on biofilm. The study question was as follows: What is the effect of SDF on biofilm?


Humeral Artery Aneurysm Revealing a Rare Association between Tuberculosis and Behçet's Disease

*Rabie Ayari, Ramy Triki, Youssef Mallat, Achraf Abdennadher, Khalil Amri, Raja Amri and Mohamed Ali Sbai*


**3.6.1 Dimeric impurity of cefotaxime**

The manufacturing and storage processes of cefotaxime produce various impurities such as dimeric impurity (Figure 2).

Fig. 2. Structure of the dimeric impurity of cefotaxime

The results of the mutagenesis assay indicate that the dimeric impurity is nonmutagenic to any test strains used in the presence and absence of S9 fraction. The results of the *in vitro* chromosomal assay show some chromosomal aberrations in cultured mammalian cells up to the maximum recommended concentration of 45 mg per culture, and no clastogenicity in mammalian cells *in vitro* (Agarwal *et al.*, 2004).

#### **3.6.2 Piperacillin impurity-A**

The piperacillin impurity-A is a prominent degradation product of piperacillin that appears during manufacturing and storage processes (Figure 3).

In all the strains of *S. typhimurium*; TA 97a, TA 98, TA 100, TA 102, and TA 1535, piperacillin impurity in the presence and absence of metabolic activation was found to be nonmutagenic. Also, *in vitro* chromosomal aberration assay did not reveal any significant alterations. It is found that piperacillin impurity-A up to 5 mg/ml is nonclastogenic to CHO cell lines in the presence and absence of metabolic activation (Vijayan *et al.*, 2007).

Tremogenic impurities comprise another sub-class of highly toxic impurities in APIs. Two pharmacopoeial APIs are known to have the potential to be contaminated with tremogenic impurities; pethidine and paroxetine (3-[(1, 3-benzodioxol-5-yloxy) methyl]-4-(4 fluorophenyl) piperidine). Pethidine can contain trace amounts of 1-methyl-4- phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) derived from the hydrolytic degradation of side chain. 4-(4- Fluorophenyl)-1-methyl-1,2,3,6-tetrahydropyridine (FMTP) can be a potential reactant/intermediate in the synthesis of paroxetine. Owing to their toxicity to cells in the *Substantia nigra*, these highly potent impurities can induce Parkinsonism in humans. Thus, these compounds are known toxic impurities; however their genotoxicity remains unclear

The following two impurities relate to the well known antibiotics cefotaxime and

The manufacturing and storage processes of cefotaxime produce various impurities such as

The results of the mutagenesis assay indicate that the dimeric impurity is nonmutagenic to any test strains used in the presence and absence of S9 fraction. The results of the *in vitro* chromosomal assay show some chromosomal aberrations in cultured mammalian cells up to the maximum recommended concentration of 45 mg per culture, and no clastogenicity in

The piperacillin impurity-A is a prominent degradation product of piperacillin that appears

In all the strains of *S. typhimurium*; TA 97a, TA 98, TA 100, TA 102, and TA 1535, piperacillin impurity in the presence and absence of metabolic activation was found to be nonmutagenic. Also, *in vitro* chromosomal aberration assay did not reveal any significant alterations. It is found that piperacillin impurity-A up to 5 mg/ml is nonclastogenic to CHO

cell lines in the presence and absence of metabolic activation (Vijayan *et al.*, 2007).

**3.5.4 Tremogenic impurities**

(Borman *et al.*, 2008).

piperacillin.

**3.6 β-lactam related impurities**

dimeric impurity (Figure 2).

**3.6.1 Dimeric impurity of cefotaxime**

Fig. 2. Structure of the dimeric impurity of cefotaxime

during manufacturing and storage processes (Figure 3).

mammalian cells *in vitro* (Agarwal *et al.*, 2004).

**3.6.2 Piperacillin impurity-A**

Fig. 3. Structure of piperacillin impurity-A


**13. Prospects and research needs**

Arsenic accumulation in rice is a substantial global concern [1, 4, 6]. The soil chemistry of arsenic accumulation in rice is rapidly being elucidated; however, studies have yet to develop consistent desirable outcomes with respect to irrigation technology, soil amendments, phytoremediation, and yield maintenance. Alternate wetting and drying and furrow irrigation are competing irrigation regimes, with research showing substantial reductions in arsenic accumulation. However, rice yield maintenance, implementing reliable nitrogen fertilization practices, and providing effective weed management programs remain problematic, especially when food security and traditions may be compounding realities. Water scarcity and climate change provide both opportunities and setbacks to altering irrigation methods [80].

The understanding of rice physiology and arsenic is beginning to be formulated. Das et al. [81] illustrated the importance of biochemical relationships involving ascorbate-glutathione cycle and thiol metabolism to support reducing yield suppression in arsenic impacted rice. Wu et al. [82] showed the promise of arsenicphosphate interactions involving phosphate transporter expression in rice. Thus, understanding arsenic root uptake at the cellular membrane level and its subsequent movement within the plant, combined with rice breeding and cultivar selection, remain clear avenues of research to reduce the human daily uptake of arsenic.

The prospect of reducing arsenic uptake rests with a global effort to: (i) produce cultivars that restrict arsenic uptake to root cells and exude arsenic to the rhizosphere, and (ii) alter irrigation practices to provide sufficient intervals of oxic soil environment to mitigate arsenic bioavailability. These approaches will also provide other environmental advantages, including water conservation and reduced methane emission [83].

*Soil Science - Emerging Technologies, Global Perspectives and Applications*