**Acknowledgements**

Two approaches have been explored to study scale-dependent elastic behavior: experimental and theoretical, however a discrepancy exists between the two approaches. The scale at which the scale-dependence starts in experimental measurements is different from that estimated theoretically. Various techniques involving resonance frequency, tensile tests in TEM, atomic force microscopy and nanoindentation have been used to characterize the effective Young's modulus of silicon nanocantilevers and nanowires. Each method involves different assump‐ tions, sources of errors and interpretations. We proposed the use of the electrostatic pull-in instability method in order to avoid most of the issues with other existing methods, as described in Section 3. Using molecular dynamics calculations and the semi-continuum approaches (Section 4) the surface effects on the elastic behavior of silicon nanocantilevers have been investigated. Direct comparison between the surface effects simulations with experi‐ mental data from the reliable EPI method showed that although surface effects influence the effective Young's modulus of silicon to some extent, they alone are insufficient to explain the

180 Advances in Micro/Nano Electromechanical Systems and Fabrication Technologies

Another important influence is caused by native oxide layers that exist at the surfaces of the silicon nanocantilevers. The native oxide layers influence the effective Young's modulus of silicon in 3 ways. Through: 1) its distinct elastic response; 2) unknown interactions between the oxide and the silicon at the interface; and, 3) consumption of silicon during oxidation; during oxidation for every unit of silicon oxide 0.44 units of the silicon surface is "consumed". We assumed that the effect of native oxide on the surface elasticity of silicon, or the interface elasticity of Si-SiO2, is not significant compared to the distinct elastic response of the native oxide. Taking the native oxide layers into consideration reduced the difference between experimental measurements and theoretical predictions, yet there was still a considerable

One of the important issues which was confirmed experimentally, but had not previously been considered in theoretical modeling, was accounting for the fact that experimentally tested nanocantilevers and nanowires are not defect free. Molecular dynamics calculations have been carried out to determine the effects of defects on the effective Young's modulus of silicon nanocantilevers. The conclusion is that the scale effect observed in silicon nanocantilevers can be explained by a contribution of surface effects, native oxide layers and defects. Taking these into account, the gap between the experimental measurements and theory can be closed.

There are a number of issues that are recommended for future research on the size effects:

**•** The electrostatic pull-in instability method has its own limitations; the application of electrostatic load requires a fairly conductive device and a counter electrode. This implies that the method is only applicable to conductive materials. Moreover, stiction prevention by additional stopper is necessary to ensure the release of the cantilever for multiple measurements. Another fact, that is believed to be minor, but has to be investigated in detail, is the influence of the electric field (electrostatic charges) used in EPI method on the measured value of the effective Young's modulus of nanocantilevers and nanowires.

**•** Future work on the silicon native oxide layer and its properties will help to explain in more detail the observed size effects. The scale-dependent elastic behavior of silicon native oxide

experimental observations.

difference (Fig. 14).

This book chapter was financially supported by Enabling Technology program (ETP) for Materials Technology, Dr. L.J.M.G. Dortmans, of Netherlands Organization for Scientific Applied Research (TNO). The authors would like to thank Dr. T.S.D. O'Mahoney and Dr. A. Bossche for their supports, helps and valuable suggestions.


**9.1. Matrix effect**

110 Chromatography – The Most Versatile Method of Chemical Analysis

low concentrations are expected in such studies [4].

drugs and their metabolites in biological samples [95-97].

**8.5. Other detection techniques**

mass spectrometry.

in the positive ionization mode in mobile phase containing acetonitrile with formic acid and/or ammonium acetate. But replacement of acetonitrile with methanol in mobile phase gave approximately 25-fold higher response. On the other hand, for the same analyte, mobile phases containing acetonitrile or methanol gave about the same response in negative ionization [79]. Another interesting example is analysis of bisphenol A and its metabolite in biological samples. In order to gain the highest possible sensitivity for bisphenol A and bisphenol A glucuronide, LC-MS/MS conditions were optimized. ESI ionization source operating in negative ionization mode was selected for further optimization of mobile phase. It was found that substitution of acetonitrile/water with methanol/water as mobile phase increased response of parent by approximately two-fold but at the same time decreased response of its metabolite by approximately three-fold. Acetonitrile was selected as organic modifier because metabolite quantification is the main concern of metabolism studies. Additionally, sufficiently high sensitivity is needed for metabolite determination as

UV, fluorescence or electrochemical detectors are usually coupled with liquid chromatograph for determination of drugs and their metabolites. Total analysis time of these methods is often long because baseline chromatographic separation is required for quantification purposes. In terms of reproducibility and robustness, UV and fluorescence detection have an advantage over mass spectrometry. However, methods are less sensitive and specific what requires extensive and time-consuming sample preparation compared to

Before the advent of mass spectrometry, UV was the primary detection technique used in pharmacokinetics for quantification of drugs and their metabolites in biological matrices. Although robust, reliable, simple and easy to use, UV detection provides relatively poor sensitivity, especially when the compound of interest has no significant chromophore [44]. However, HPLC coupled with UV detection is still widely applicable for determination of

In contrast to UV, fluorescence or electrochemical detection can be a very selective and sensitive detection technique. These detectors can extend the sensitivity by 1-3 orders of magnitude if the analyte exhibits, or can be readily derivatized to exhibit, fluorescence or electroactivity [7]. Some drugs such as morphine have good fluorophores which allows its detection without derivatization. For direct determination of morphine and its two glucuronides assays based on liquid chromatography with different detector systems (UV, fluorescence, electrochemical, MS) has been reported. Limits of quantifications for both metabolites were comparable for MS and fluorescence detection but were as expected higher for UV detection [98]. Oxidation vulnerability and native fluorescence properties of most biogenic amines may explain the long history of their quantification by these conventional HPLC detection methods. However, LC-MS/MS methods are rapidly emerging due to its Matrix effect (ME) is a term that describes any changes in the MS response of analyte that can lead to either a reduced response (ion suppression) or an increased response (ion enhancement) of the LC-MS system. ME is caused by molecules originating from the sample matrix or mobile phase that co-elute with the analyte of interest and therefore interfere with the ionization process in the MS ion source. Several approaches have been proposed to evaluate ME. Among them the post column infusion technique is widely used. Use of this qualitative evaluation technique allows the determination of the matrix effect of endogenous components in blank matrix. During analysis of blank matrix, analyte response is monitored to provide information where in the chromatographic run interferences between the analyte and matrix compounds occur. ME is illustrated as response deviation in the otherwise flat response time trace of the continuously post-column infused analyte [104]. This approach is very useful during method development because it provides information on the retention times where ME has to be expected, which can later be avoided for analyte of interest by optimizing chromatographic conditions.

For quantitative estimation of ME another well recognised approach is more suitable. Matuszewski et al. reported practical approach for the assessment of the absolute and relative ME as a part of validation of bioanalytical LC-MS/MS methods [94]. The difference in response between neat solution sample and post-extraction spiked sample is called the absolute ME, while difference between various lots of post-spiked samples is called the relative ME. As such will an absolute ME primary affect the accuracy and relative ME will affect the precision of the method. The determination of a relative ME is much more important than the determination of absolute ME in the evaluation and validation of bioanalytical method in biofluids [94]. The relative ME caused by interindividual variability in the sample matrix is assessed based on at least 5 lots of different matrices. Relative ME can be expressed as a coefficient of variation at particular concentration level or calculated based on slope lines. For the method to be considered reliable and free from the relative ME, the calculated coefficient of variation of determined slopes in different sources of matrices should not exceed 3-4% [105].

Analytical Methods for Quantification of Drug Metabolites in Biological Samples 113

However, the most efficient way to eliminate the influence of ME on the accuracy and precision of a quantitative analytical method is the use of stable isotope labeled analogs as

The selection of a suitable internal standard (IS) is one of the key parameters for establishing a successful LC-MS/MS method. Usually, stable isotope, such as 2H (D, deuterium), 13C, 15N or 17O, labeled standards are most appropriate for compensation of possible loss of analytes during sample clean up and variations in instrument performance (typically caused by matrix effect) since their physicochemical characteristics are practically identical to that of unlabeled analyte. In general, a stable isotope labeled IS is considered to be ideal, since it shows almost same behavior to the analyte of interest in sample preparation, chromatography as well as in ionization process [102]. However, issues like isotopic purity of IS, cross-contamination and cross-talk between MS ion channels, as well as IS stability and isotopic integrity of the label in biological fluid and during sample preparation should

Mass difference between analyte and IS should be at least 3 Da to avoid signal contribution of the natural isotopes to the signal of IS. Although deuterated IS are most frequently used, several disadvantages need to be considered in some cases, such as different extraction recoveries and retention times between such IS and analyte or exchange of deuterium atoms by hydrogen atoms [108]. For example, differences in retention times for deuterium labeled (d16) and unlabeled bisphenol A compounds is shown in Figure 2, where both, labeled metabolite and parent compound eluted slightly before their unlabeled analogs. Interestingly to mention, deuterium labeled bisphenol A (d16) has all 16 hydrogen atoms substituted by deuterium atoms but is actually d14 labeled (mass shift 14 Da in MRM transition, Figure 2) because two deuterium atoms from the functional group (–OD) are easily exchangeable by hydrogen atoms. The observable chromatographic retention time shifts for deuterated analogs depend on the number of deuterated atoms in structure. Deuterated analogs have no retention time shifts up to approximately six deuterium atoms [34] but when more deuterium atoms are included in structure, such as ten, the retention time shift may be significant (up to 1.2 min) [108]. Therefore, other stable isotope labeled IS, such as 13C, are considered as more appropriate

The concentration of IS used for sample preparation is also important and should be approximately at the middle of calibration curve. Optimization of IS concentration is critical to avoid ion suppression by co-eluting analyte leading to standard curve nonlinearity [70]. Even if stable labeled isotope IS is used, ME should still be investigated. Namely if ion suppression significantly reduces the signal of both, analyte and IS, the signal to noise ratio may decrease to a point where accuracy and precision may be

internal standards [105].

be carefully addressed [94].

[108].

negatively affected [102].

**9.2. Selection of internal standard**

ME is known to be both component and matrix dependent. It was demonstrated that matrix induced ion suppression is especially important for early eluting compounds, such as polar metabolites. Typically, ME more strongly influences lower than higher analyte concentrations [71]. The main source of the commonly observed ME of plasma samples is believed to be endogenous phospholipids and proteins. The lysophospholipids which normally elute earlier in reversed phase chromatography are more likely to cause matrix effects compared to the later eluting phospholipids in spite of the larger concentrations of the latter in plasma [106]. Phospholipids cause ion suppression in both, positive and negative ESI modes and must be removed or resolved chromatographically.

To remove or reduce ME, modification to the sample extraction methodology (SPE or LLE instead of PP) and improved chromatographic separation must be performed. The majority of ME occur in the solvent front of a chromatographic run and if the analytes can be retained to some degree, matrix effects can be minimized. Suitable sample preparation and chromatographic conditions are linked together and form the basis of developing a successful and robust quantitative LC-MS/MS method [102, 103]. Another consideration when dealing with ME is selection of ionization interface. APCI is generally considered to be less prone to ion suppression compared with ESI [94, 105]. However, assay sensitivity and thermal stability of the analyte should be evaluated for eventual APCI application. Reducing the flow rate [20 µl/min or below) directed to ESI source by post column splitting may also reduce or completely remove the ion suppression [107]. Additionally to other approaches UHPLC technology in combination with polymeric mixed-mode SPE and appropriate mobile phase pH may provide significant advantages for reducing ME [73]. Mobile phase additives such as triethylamine and trifluoroacetic acid can also lead to ion suppression. The use of other reagents such as formic or acetic acid, trifluoroacetic acid in conjunction with 10 mM ammonium acetate or addition of 1% propionic acid to the mobile phase may overcome the ME of trifluoroacetic acid. Triethylamine may be replaced with other ion paring reagent such as hexylamine [70].

However, the most efficient way to eliminate the influence of ME on the accuracy and precision of a quantitative analytical method is the use of stable isotope labeled analogs as internal standards [105].

## **9.2. Selection of internal standard**

112 Chromatography – The Most Versatile Method of Chemical Analysis

should not exceed 3-4% [105].

chromatographically.

such as hexylamine [70].

For quantitative estimation of ME another well recognised approach is more suitable. Matuszewski et al. reported practical approach for the assessment of the absolute and relative ME as a part of validation of bioanalytical LC-MS/MS methods [94]. The difference in response between neat solution sample and post-extraction spiked sample is called the absolute ME, while difference between various lots of post-spiked samples is called the relative ME. As such will an absolute ME primary affect the accuracy and relative ME will affect the precision of the method. The determination of a relative ME is much more important than the determination of absolute ME in the evaluation and validation of bioanalytical method in biofluids [94]. The relative ME caused by interindividual variability in the sample matrix is assessed based on at least 5 lots of different matrices. Relative ME can be expressed as a coefficient of variation at particular concentration level or calculated based on slope lines. For the method to be considered reliable and free from the relative ME, the calculated coefficient of variation of determined slopes in different sources of matrices

ME is known to be both component and matrix dependent. It was demonstrated that matrix induced ion suppression is especially important for early eluting compounds, such as polar metabolites. Typically, ME more strongly influences lower than higher analyte concentrations [71]. The main source of the commonly observed ME of plasma samples is believed to be endogenous phospholipids and proteins. The lysophospholipids which normally elute earlier in reversed phase chromatography are more likely to cause matrix effects compared to the later eluting phospholipids in spite of the larger concentrations of the latter in plasma [106]. Phospholipids cause ion suppression in both, positive and negative ESI modes and must be removed or resolved

To remove or reduce ME, modification to the sample extraction methodology (SPE or LLE instead of PP) and improved chromatographic separation must be performed. The majority of ME occur in the solvent front of a chromatographic run and if the analytes can be retained to some degree, matrix effects can be minimized. Suitable sample preparation and chromatographic conditions are linked together and form the basis of developing a successful and robust quantitative LC-MS/MS method [102, 103]. Another consideration when dealing with ME is selection of ionization interface. APCI is generally considered to be less prone to ion suppression compared with ESI [94, 105]. However, assay sensitivity and thermal stability of the analyte should be evaluated for eventual APCI application. Reducing the flow rate [20 µl/min or below) directed to ESI source by post column splitting may also reduce or completely remove the ion suppression [107]. Additionally to other approaches UHPLC technology in combination with polymeric mixed-mode SPE and appropriate mobile phase pH may provide significant advantages for reducing ME [73]. Mobile phase additives such as triethylamine and trifluoroacetic acid can also lead to ion suppression. The use of other reagents such as formic or acetic acid, trifluoroacetic acid in conjunction with 10 mM ammonium acetate or addition of 1% propionic acid to the mobile phase may overcome the ME of trifluoroacetic acid. Triethylamine may be replaced with other ion paring reagent The selection of a suitable internal standard (IS) is one of the key parameters for establishing a successful LC-MS/MS method. Usually, stable isotope, such as 2H (D, deuterium), 13C, 15N or 17O, labeled standards are most appropriate for compensation of possible loss of analytes during sample clean up and variations in instrument performance (typically caused by matrix effect) since their physicochemical characteristics are practically identical to that of unlabeled analyte. In general, a stable isotope labeled IS is considered to be ideal, since it shows almost same behavior to the analyte of interest in sample preparation, chromatography as well as in ionization process [102]. However, issues like isotopic purity of IS, cross-contamination and cross-talk between MS ion channels, as well as IS stability and isotopic integrity of the label in biological fluid and during sample preparation should be carefully addressed [94].

Mass difference between analyte and IS should be at least 3 Da to avoid signal contribution of the natural isotopes to the signal of IS. Although deuterated IS are most frequently used, several disadvantages need to be considered in some cases, such as different extraction recoveries and retention times between such IS and analyte or exchange of deuterium atoms by hydrogen atoms [108]. For example, differences in retention times for deuterium labeled (d16) and unlabeled bisphenol A compounds is shown in Figure 2, where both, labeled metabolite and parent compound eluted slightly before their unlabeled analogs. Interestingly to mention, deuterium labeled bisphenol A (d16) has all 16 hydrogen atoms substituted by deuterium atoms but is actually d14 labeled (mass shift 14 Da in MRM transition, Figure 2) because two deuterium atoms from the functional group (–OD) are easily exchangeable by hydrogen atoms. The observable chromatographic retention time shifts for deuterated analogs depend on the number of deuterated atoms in structure. Deuterated analogs have no retention time shifts up to approximately six deuterium atoms [34] but when more deuterium atoms are included in structure, such as ten, the retention time shift may be significant (up to 1.2 min) [108]. Therefore, other stable isotope labeled IS, such as 13C, are considered as more appropriate [108].

The concentration of IS used for sample preparation is also important and should be approximately at the middle of calibration curve. Optimization of IS concentration is critical to avoid ion suppression by co-eluting analyte leading to standard curve nonlinearity [70]. Even if stable labeled isotope IS is used, ME should still be investigated. Namely if ion suppression significantly reduces the signal of both, analyte and IS, the signal to noise ratio may decrease to a point where accuracy and precision may be negatively affected [102].

Problems may occur when more than one compound is determined in the same analytical method. A number of labeled IS identical to number of analyzed compounds would in this case be required [102]. This is not always practically feasible, especially not for stable isotope labeled drug metabolites as their availability is very limited. In such cases, to assure the suitability of the quantification method for determination of drugs and their metabolites, ME evaluation should be carefully addressed [37]. On the other hand, example for simultaneous determination of parent compound and its metabolite using both labeled IS [4] is shown in Figure 2. It is preferred that the labeled IS product ion used for the MRM transition retains the stable isotope moiety as for m/z 403 → 227 (bisphenol A-glucuronide) versus 417 → 241 (deuterated bisphenol A-glucuronide) in Figure 2.

Analytical Methods for Quantification of Drug Metabolites in Biological Samples 115

during the validation of the method. Carry-over is usually expressed as percentage of detected analyte in blank sample regarding to its limit of quantification [4] and may

**Figure 2.** LC-MS/MS chromatogram in MRM mode for metabolite (BPAG) and parent bisphenol A (BPA) of a typical microsomal incubation. (A) mass transition for BPAGd16 (internal standard for BPAG), (B) mass transition for BPAG, (C) mass transition for BPAd16 (internal standard for BPA), (D) mass

transition for BPA. For analysis conditions refer to [4].

significantly lower the sensitivity of the method [109].

However, labeled ISs are not always available or can be very expensive. As an alternative, structural analogues can be used, with consideration of the structural similarities between the IS and the analyte. To be suitable, the ionization of the analogue must be compared with analyte and should preferably co-elute with the analyte (Figure 1). The selected analog IS should not correspond to any metabolic product of analyte, such as hydroxylated or Ndealkylated metabolites [108]. Nevertheless, in many cases analog ISs or structurally unrelated ISs are not able to compensate the ME [94, 105]. In such situation, other more rigorous approaches to reduce or eliminate ME should be applied (see 9.1.).

## **9.3. LC-MS pitfalls**

Metabolite may produce a molecular ion that is identical to parent ion through in source conversion. Typical metabolites for such conversion are glucuronides, sulfates or N-oxides [79]. N-oxide conversion may also serve for identification purposes as this transformation takes place in APCI source and represent another potential way to differentiate N-oxide from hydroxylated metabolites (both with exact mass) since the later usually do not undergo thermal deoxygenation [23]. Therefore, an inadequate chromatographic separation between parent drug and such metabolites will result in over estimation of parent drug concentration in the presence of these isomeric compounds. The same situation may happen also by ion channel cross-talk in MRM mode, which means that fragment issued from the other scanned transition is still present in collision cell. Metabolites, such as diverse glucuronides, that give the same product ion as parent drug, have to be therefore chromatographically separated. However, for co-eluting compounds, such as analytes and their IS, the absence of cross-talk has to be demonstrated [37].

Carry-over, which is the appearance of an analyte signal in a blank injection subsequently to analysis of high concentration samples, is also a common problem in LC-MS/MS methods. This problem occurs due to retention of analytes by adsorption on active surfaces of the autosamples, solvent lines, extraction columns (e.g. online SPE) or the analytical column. Most carry-over problems can be minimized by an appropriate choice of injector wash solutions and methods, by proper choice of mobile phase and tubings, and by choice of suitable stationary phase and proper variation in the solvent strength [70]. Carry-over becomes prominent after injection of analyte at high concentration and should be assessed during the validation of the method. Carry-over is usually expressed as percentage of detected analyte in blank sample regarding to its limit of quantification [4] and may significantly lower the sensitivity of the method [109].

114 Chromatography – The Most Versatile Method of Chemical Analysis

versus 417 → 241 (deuterated bisphenol A-glucuronide) in Figure 2.

rigorous approaches to reduce or eliminate ME should be applied (see 9.1.).

**9.3. LC-MS pitfalls**

has to be demonstrated [37].

Problems may occur when more than one compound is determined in the same analytical method. A number of labeled IS identical to number of analyzed compounds would in this case be required [102]. This is not always practically feasible, especially not for stable isotope labeled drug metabolites as their availability is very limited. In such cases, to assure the suitability of the quantification method for determination of drugs and their metabolites, ME evaluation should be carefully addressed [37]. On the other hand, example for simultaneous determination of parent compound and its metabolite using both labeled IS [4] is shown in Figure 2. It is preferred that the labeled IS product ion used for the MRM transition retains the stable isotope moiety as for m/z 403 → 227 (bisphenol A-glucuronide)

However, labeled ISs are not always available or can be very expensive. As an alternative, structural analogues can be used, with consideration of the structural similarities between the IS and the analyte. To be suitable, the ionization of the analogue must be compared with analyte and should preferably co-elute with the analyte (Figure 1). The selected analog IS should not correspond to any metabolic product of analyte, such as hydroxylated or Ndealkylated metabolites [108]. Nevertheless, in many cases analog ISs or structurally unrelated ISs are not able to compensate the ME [94, 105]. In such situation, other more

Metabolite may produce a molecular ion that is identical to parent ion through in source conversion. Typical metabolites for such conversion are glucuronides, sulfates or N-oxides [79]. N-oxide conversion may also serve for identification purposes as this transformation takes place in APCI source and represent another potential way to differentiate N-oxide from hydroxylated metabolites (both with exact mass) since the later usually do not undergo thermal deoxygenation [23]. Therefore, an inadequate chromatographic separation between parent drug and such metabolites will result in over estimation of parent drug concentration in the presence of these isomeric compounds. The same situation may happen also by ion channel cross-talk in MRM mode, which means that fragment issued from the other scanned transition is still present in collision cell. Metabolites, such as diverse glucuronides, that give the same product ion as parent drug, have to be therefore chromatographically separated. However, for co-eluting compounds, such as analytes and their IS, the absence of cross-talk

Carry-over, which is the appearance of an analyte signal in a blank injection subsequently to analysis of high concentration samples, is also a common problem in LC-MS/MS methods. This problem occurs due to retention of analytes by adsorption on active surfaces of the autosamples, solvent lines, extraction columns (e.g. online SPE) or the analytical column. Most carry-over problems can be minimized by an appropriate choice of injector wash solutions and methods, by proper choice of mobile phase and tubings, and by choice of suitable stationary phase and proper variation in the solvent strength [70]. Carry-over becomes prominent after injection of analyte at high concentration and should be assessed

**Figure 2.** LC-MS/MS chromatogram in MRM mode for metabolite (BPAG) and parent bisphenol A (BPA) of a typical microsomal incubation. (A) mass transition for BPAGd16 (internal standard for BPAG), (B) mass transition for BPAG, (C) mass transition for BPAd16 (internal standard for BPA), (D) mass transition for BPA. For analysis conditions refer to [4].

## **9.4. Metabolite stability**

For drugs and metabolites that are unstable, with one converting to other, conditions used during sample preparation and analysis must be optimized in order to minimize such conversion and to achieve accurate quantification of the drug and metabolite. Common factors that affect drug and drug metabolite stability in biological matrices include temperature, light, pH, oxidation and enzymatic degradation [110]. Acyl glucuronides (Oester conjugates) are probably the most commonly encountered problematic metabolites in bioanalysis. Acyl glucuronides are unstable and hydrolyze to release aglycone under neutral and alkaline conditions. Different acyl glucuronides have been shown to have different rates of hydrolysis. However, mildly acidic conditions (pH 3-5) should be the most desirable pH for minimizing the reaction in biological samples [59, 111]. Acyl glucuronides (1-O-acyl glucuronides) are also susceptible to internal migration under both, physiological and alkaline conditions. The rate of migration resulting in 2-, 3- and 4- O-acyl glucuronides increases with increasing pH and temperature. Such isomeric glucuronides are not susceptible to hydrolysis by ß-glucuronidases and may compromise the quantification of metabolites and total parent drug when indirect quantification approach via conjugate cleavage is applied [111]. Similar to O-glucuronides, Nglucuronides can be converted back to parent drug under acidic/basic/neutral pH conditions or at elevated sample processing temperature, but this instability is largely compound dependent. For example olanzapine glucuronide can be readily cleaved under acidic conditions, clozapine and cyclizine glucuronides are unstable at range pH 1-3 and doxepin glucuronide at pH 11 [112]. Lactone is another commonly unstable metabolite function group which may be converted to its open ring hydroxy acid drug. Lactone metabolites, such as atorvastatin metabolite, require optimization of the sample pH (typically 3-5) in order to minimize the hydrolysis of lactone metabolite back to parent drug [113].

Analytical Methods for Quantification of Drug Metabolites in Biological Samples 117

successful monitoring of the drug metabolism, suitable bioanalytical methods have to be developed and validated. Liquid chromatography coupled with mass spectrometry has become the most powerful analytical tool for identification and quantification of drug

The known identity of metabolites is the prerequisite for a suitable metabolic assessment of drugs. Appropriate LC-MS instrumentation is clearly critical to both, detection and structural elucidation. Tandem mass spectrometry instruments are beside their key role for metabolite quantification also well suited for qualitative purposes as they enable different scan possibilities (constant neutral loss, precursor ion, product ion) for structural characterization of metabolites. However, the drive to more versatile and powerful instruments which can perform intelligent data dependent experiments and accurate mass measurements has led to newer high resolution mass analyzers, such as Q-TOF instruments,

Direct quantification of metabolites in biological samples is the most appropriate approach, but also others approaches such as indirect quantification through parent drug after metabolite hydrolysis or quantification supported by using response factors may be used which primary depends on the availability of suitable authentic standards. Analytical methods for metabolite quantification are based on liquid chromatography or other separation techniques coupled with various detector systems where LC-MS/MS plays predominant role in bioassays for pharmacokinetic and metabolism studies due to its

In order to support metabolism experiments in a timely manner, the use of high throughput methods for the analysis of drugs and their metabolites in biological samples has become an essential part, especially in the most time consuming sample preparation. Trend is going toward semi automated off-line sample treatment in 96-well plate format or on-line SPE after direct injection of samples. Additionally, other high throughput approaches can be introduced. Ultra-high performance liquid chromatography with small particles and monolithic chromatography offer improvements in speed, resolution and sensitivity compared to conventional chromatographic techniques. Hydrophilic interaction chromatography (HILIC) or other specialized columns suitable for polar metabolites are emerging as a valuable supplement to classical reversed phase

The main advantage of LC-MS/MS allows development of high throughput methods with little or no sample preparation and minimal chromatographic retention. However, matrix effect may have a significant impact on such analyses. Matrix effect issue is frequently underestimated and should be adequately addressed. Not without reason, matrix effect have been called the Achilles heel of quantitative LC-ESI-MS/MS [103]. The use of stable isotope labeled analog as internal standard is the most efficient way to reduce matrix effect. But normally, additional approaches to reduce or eliminate matrix effect are

which now dominate the metabolite identification field.

inherent specificity, sensitivity and speed.

chromatography.

needed.

metabolites.

N-oxides are also unstable in solutions and biological samples during sample preparation, especially under strong acidic or basic conditions. Light exposure may further accelerate the decomposition of these metabolites. Other compounds are also susceptible to photodegradation such as, catechols, nisoldipine, rifampin and their metabolites and should be protected from light during sample preparation and analysis [112].

## **10. Conclusion**

Current drug discovery efforts have been focused on identification of drug metabolism and pharmacokinetic issues at the earliest possible stage in order to reduce the attrition rate of drug candidates during the developmental phase. Metabolic fate of drugs can be responsible for problems associated with their bioavailability, interindividual variability, drug-drug interactions, pharmacologic activity or the toxicity. Different *in vitro* methods, from subcelullar to organ range, and *in vivo* studies are applied for the clarification of drug metabolism. Among them microsomes and hepatocytes are the most frequently utilized *in vitro* models in drug metabolic profiling and drug interaction studies. For the successful monitoring of the drug metabolism, suitable bioanalytical methods have to be developed and validated. Liquid chromatography coupled with mass spectrometry has become the most powerful analytical tool for identification and quantification of drug metabolites.

116 Chromatography – The Most Versatile Method of Chemical Analysis

For drugs and metabolites that are unstable, with one converting to other, conditions used during sample preparation and analysis must be optimized in order to minimize such conversion and to achieve accurate quantification of the drug and metabolite. Common factors that affect drug and drug metabolite stability in biological matrices include temperature, light, pH, oxidation and enzymatic degradation [110]. Acyl glucuronides (Oester conjugates) are probably the most commonly encountered problematic metabolites in bioanalysis. Acyl glucuronides are unstable and hydrolyze to release aglycone under neutral and alkaline conditions. Different acyl glucuronides have been shown to have different rates of hydrolysis. However, mildly acidic conditions (pH 3-5) should be the most desirable pH for minimizing the reaction in biological samples [59, 111]. Acyl glucuronides (1-O-acyl glucuronides) are also susceptible to internal migration under both, physiological and alkaline conditions. The rate of migration resulting in 2-, 3- and 4- O-acyl glucuronides increases with increasing pH and temperature. Such isomeric glucuronides are not susceptible to hydrolysis by ß-glucuronidases and may compromise the quantification of metabolites and total parent drug when indirect quantification approach via conjugate cleavage is applied [111]. Similar to O-glucuronides, Nglucuronides can be converted back to parent drug under acidic/basic/neutral pH conditions or at elevated sample processing temperature, but this instability is largely compound dependent. For example olanzapine glucuronide can be readily cleaved under acidic conditions, clozapine and cyclizine glucuronides are unstable at range pH 1-3 and doxepin glucuronide at pH 11 [112]. Lactone is another commonly unstable metabolite function group which may be converted to its open ring hydroxy acid drug. Lactone metabolites, such as atorvastatin metabolite, require optimization of the sample pH (typically 3-5) in order to minimize the hydrolysis of lactone metabolite back to parent

N-oxides are also unstable in solutions and biological samples during sample preparation, especially under strong acidic or basic conditions. Light exposure may further accelerate the decomposition of these metabolites. Other compounds are also susceptible to photodegradation such as, catechols, nisoldipine, rifampin and their metabolites and should

Current drug discovery efforts have been focused on identification of drug metabolism and pharmacokinetic issues at the earliest possible stage in order to reduce the attrition rate of drug candidates during the developmental phase. Metabolic fate of drugs can be responsible for problems associated with their bioavailability, interindividual variability, drug-drug interactions, pharmacologic activity or the toxicity. Different *in vitro* methods, from subcelullar to organ range, and *in vivo* studies are applied for the clarification of drug metabolism. Among them microsomes and hepatocytes are the most frequently utilized *in vitro* models in drug metabolic profiling and drug interaction studies. For the

be protected from light during sample preparation and analysis [112].

**9.4. Metabolite stability**

drug [113].

**10. Conclusion**

The known identity of metabolites is the prerequisite for a suitable metabolic assessment of drugs. Appropriate LC-MS instrumentation is clearly critical to both, detection and structural elucidation. Tandem mass spectrometry instruments are beside their key role for metabolite quantification also well suited for qualitative purposes as they enable different scan possibilities (constant neutral loss, precursor ion, product ion) for structural characterization of metabolites. However, the drive to more versatile and powerful instruments which can perform intelligent data dependent experiments and accurate mass measurements has led to newer high resolution mass analyzers, such as Q-TOF instruments, which now dominate the metabolite identification field.

Direct quantification of metabolites in biological samples is the most appropriate approach, but also others approaches such as indirect quantification through parent drug after metabolite hydrolysis or quantification supported by using response factors may be used which primary depends on the availability of suitable authentic standards. Analytical methods for metabolite quantification are based on liquid chromatography or other separation techniques coupled with various detector systems where LC-MS/MS plays predominant role in bioassays for pharmacokinetic and metabolism studies due to its inherent specificity, sensitivity and speed.

In order to support metabolism experiments in a timely manner, the use of high throughput methods for the analysis of drugs and their metabolites in biological samples has become an essential part, especially in the most time consuming sample preparation. Trend is going toward semi automated off-line sample treatment in 96-well plate format or on-line SPE after direct injection of samples. Additionally, other high throughput approaches can be introduced. Ultra-high performance liquid chromatography with small particles and monolithic chromatography offer improvements in speed, resolution and sensitivity compared to conventional chromatographic techniques. Hydrophilic interaction chromatography (HILIC) or other specialized columns suitable for polar metabolites are emerging as a valuable supplement to classical reversed phase chromatography.

The main advantage of LC-MS/MS allows development of high throughput methods with little or no sample preparation and minimal chromatographic retention. However, matrix effect may have a significant impact on such analyses. Matrix effect issue is frequently underestimated and should be adequately addressed. Not without reason, matrix effect have been called the Achilles heel of quantitative LC-ESI-MS/MS [103]. The use of stable isotope labeled analog as internal standard is the most efficient way to reduce matrix effect. But normally, additional approaches to reduce or eliminate matrix effect are needed.


*Article* **Electrospun Poly(ethylene-***co***-vinyl alcohol)/Graphene Nanoplatelets Composites of Interest in Intelligent Food Packaging Applications**

**Sergio Torres-Giner 1, Yolanda Echegoyen 1, Roberto Teruel-Juanes 2, Jose D. Badia 2,3, Amparo Ribes-Greus 2 and Jose M. Lagaron 1,\***

1 Novel Materials and Nanotechnology Group, Institute of Agrochemistry and Food Technology (IATA), Spanish Council for Scientific Research (CSIC), Calle Catedrático Agustín Escardino Benlloch 7, 46980Paterna,Spain;storresginer@iata.csic.es(S.T.-G.);yolanda.echegoyen@iata.csic.es(Y.E.)

Received: 1 August 2018; Accepted: 19 September 2018; Published: 20 September 2018

**Abstract:** Graphene nanoplatelets (GNPs) were synthetized from graphite powder and, thereafter, embedded in poly(ethylene-*co*-vinyl alcohol) (EVOH) fibers by electrospinning in the 0.1–2 wt.-% range. The morphological, chemical, and thermal characterization performed on the electrospun nanocomposite fibers mats revealed that the GNPs were efficiently dispersed and rolled along the EVOH fibrilar matrix up to contents of 0.5 wt.-%. Additionally, the dielectric behavior of the nanocomposite fibers was evaluated as a function of the frequency range and GNPs content. The obtained results indicated that their dielectric constant rapidly decreased with the frequency increase and only increased at low GNPs loadings while the nanocomposite fiber mats became electrically conductive, with the maximum at 0.5 wt.-% GNPs content. Finally, the electrospun mats were subjected to a thermal post-treatment and dark films with a high contact transparency were obtained, suggesting that the nanocomposites can be used either in a nonwoven fibers form or in a continuous film form. This study demonstrates the potential of electrospinning as a promising technology to produce GNPs-containing materials with high electrical conductivity that can be of potential interest in intelligent packaging applications as "smart" labels or tags.

**Keywords:** EVOH; graphene; electrospinning; smart labels; intelligent packaging


**1. Introduction**

The manufacture of composite materials using ecologically friendly technologies is of great of interest to many academic and industrial practitioners in the areas of polymer science and engineering [1]. This trend is mainly driven by environmental considerations regarding the negative impact of petroleum-derived materials on the environment after their end-use, which are difficult to decompose in a landfill, and also by aiming to attain composite materials that possess the desired properties [2]. Composite materials often contain bio-based polymer. Among the bio-based polymers, polylactic acid (PLA) is the best biopolymer alternative for petro-polymers because of its renewability, biodegradability, biocompatibility, and good thermomechanical performance [3,4]. This biopolymer is derived from plants, such as corn and cassava, and it is known to have a relatively high melting point, strength, and versatility, with performance characteristics similar to synthetic polymers, such as polyethylene terephthalate, polyethylene, etc., [5–7].

PLA is regarded as a valuable and important biopolymer that can be utilized to replace synthetic counterparts in many applications, ranging from automotive parts to electronic devices [8–10]. However, PLA has some limitations that consequently restrict its widespread application, involving a low thermal resistance, heat distortion temperature, and crystallization rate, whereas other specific properties may be necessary in some

**Citation:** Tanjung, F.A.; Arifin, Y.; Kuswardani, R.A. Influence of Newly Organosolv Lignin-Based Interface Modifier on Mechanical and Thermal Properties, and Enzymatic Degradation of Polylactic Acid/ Chitosan Biocomposites. *Polymers* **2021**, *13*, 3355. https://doi.org/ 10.3390/polym13193355

Academic Editors: Domenico Acierno and Antonella Patti

Received: 15 September 2021 Accepted: 27 September 2021 Published: 30 September 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/).

different use sectors [11]. As a result, the incorporation of reinforcement agents, such as natural fibers and inorganic substances, represents a broad method for extending and improving the PLA's performance, particularly with regard to its tensile and thermal properties [12–16].

Many natural fibers have been studied in the design of PLA biocomposites that show promising results. Among existing natural fibers, chitosan has demonstrated excellent mechanical and thermal properties that are comparable to cellulose [17]. Chitosan is derived from chitin, which is obtained from the shells of crustaceans, such as crabs, shrimp, and prawns [18]. This natural fiber has been widely used in a variety of scientific applications, including medicine, biotechnology, the textile and food industries, as well as in fiber and plastic applications [19–21]. However, the main disadvantages of utilizing it as a reinforcing agent in polymer composites include low dispersion and poor interfacial adhesion, both of which are caused by incompatibility with the hydrophobic matrix polymer. This is demonstrated by the difficulties of the polar hydroxyl groups located on the chitosan surface in building a well-bonded interface with a nonpolar matrix polymer, as strength improvement is dependent on stress transfer at the composite's interface when an external force is applied [22]. If the interface is poor, the fiber-matrix adhesion will diminish with no enhancement in performance [23]. Consequently, this problem reduces the benefits of using potential reinforcements in polymer composites.

To address the issue of interaction, the interfacial adhesion in the composite material is chemically altered. Chemical modification has been frequently utilized to enhance the interfacial adhesion in composite systems, since it is an effective technique for reducing the hydrophilic properties of natural fibers. Previous research used treated chitosan fiber as a natural filler in polypropylene composites. The results show that the incorporation of chitosan fiber into the composites increased the tensile modulus and the impact strength, while decreasing the tensile strength significantly [24]. Therefore, the current research is focused on employing chitosan fibers to increase the performances of PLA biocomposites by filler surface modification using a newly developed modifying agent based on grafted organosolv lignin. To the best of our knowledge, investigations involving the utilization of grafted organosolv-lignin-based modifying agents in biocomposites have been less reported in the literature.

Lignin is well-known as a byproduct of the wood pulping process and is one of the abundant vegetal-derived compounds [25]. Because of its complex aromatic structure, which is linked by an ester-bridge, lignin is a highly stable polymer. It has a strong polarity that results from the existence of a huge number of hydroxyl groups, both aliphatic and aromatic [26–28]. The lignin used in this study was obtained from lignocellulosic fiber using an established organosolv method with organic solvent and water [29]. The procedure produces lignin with a low molecular weight and a huge number of reaction sites available, making this kind of lignin a suitable surface-modifying agent. However, because of the complexity of its structure, lignin is difficult to dissolve in conventional solvents, causing the limitation of its chemical reactivity [30]. Therefore, a simple copolymerization reaction with acrylic acid was used to improve the lignin reactivity and solubility. The lignin alteration produces a pendant carboxylic moiety, which provides a site for additional reactive reactions.

This research is aimed at studying the effects of chitosan fiber and a newly developed modifying agent based on grafted organosolv lignin on the mechanical and thermal performances of PLA/chitosan biocomposites. Furthermore, the weight loss of the PLA/chitosan biocomposites during enzymatic degradation is examined.

#### **2. Materials and Method**

#### *2.1. Raw Materials*

PLA (TT Biotechnology Sdn. Bhd, Penang, Malaysia) had a melt flow index of 5.6 g/10 min (180 ◦C/2160 g), and a density of 1.27 g·cm−3 . The chitosan (Hunza Nutriceuticals Sdn Bhd., Parit Buntar, Malaysia) had an average size of 80 µm and a 90% degree of

deacetylation (DD). The characteristics of chitosan are listed in Table 1. Commercial-grade diastase (sourced from malt) was supplied by Sigma Aldrich (St. Louis, MO, USA). The ethanol (98%.*v*/*v*), hydrochloric acid, *t*-butyl peroxide, acrylic acid, acetic acid, NaOH, sodium acetate and the sulphuric acid (98%.*v*/*v*) were obtained from Sigma Aldrich and used without further purification.

**Table 1.** Physical and chemical characteristics of chitosan.

#### *2.2. Extraction of Organosolv Lignin (OSL) from Lignocellulosic Fiber*

The extraction procedure of organosolv lignin (OSL) from lignocellulosic fiber was conducted following the established organosolv method [31]. The fiber was first treated using a mixture of aqueous ethanol and a catalyst (sulphuric acid) at a temperature set of 190 ◦C for 1 h, with the solid to liquid ratio adjusted at 1:8. The pretreated fiber was then rinsed with aqueous ethanol. The washes were mixed, and 5 vol% of distilled water was added to precipitate the organosolv lignin. The OSL was centrifuged and then dried in an oven at a temperature of 80 ◦C for 24 h.


**2.4.2 Organophosphate pesticides analyses**

5 g of muscle sample was shaken with 60 ml dichloromethane and 10 g anhydrous sulfate. Mix was then filtered trough a Whatman 1 PS membrane, and evaporated under vacuum at 40°C. Dry samples were diluted in 3 ml ethanol, and underwent an ultrasonic step. Extract was then purified with a Sep pack R 300 (Silica Waters, 020810; 500 mg) column conditioned with 2 ml methanol and 2 ml ethanol. 2 ml dichloromethane were used for column elution. Purified samples were dried and diluted with 3 ml dichloromethane. Organophosphate (OP) and 2 carbamates (CA) pesticides (Dichlorvos, Carbofuran, Mevinphos, Phorate, Phorate oxon, Phorate sulfone, Methiocarbe, Terbufos, Diazinon, Disulfoton, Chlorpyriphos methyl, Chlorpyriphos ethyl, Fenitrothion, Pyrimiphos methyl, Malathion, Fenthion, Parathion, Methidathion, Disulfoton sulfone, Triazophos) concentrations were determined by GC/MS in SIM mode (OP + carbofuran and methiocarbe). A 5973N MS coupled with a 6890 GC (Agilent®) was used, with a 30m HP5-MS column (0.25 mm ID, 0.25µm thickness). For each samples standard and spiked sample, 2 µL were injected. The temperature program was 100°C (2 min), 55°C/min up to 200 °C (held for 5 min), 50 °C up to 220 °C (held for 3 minutes), followed by 60 °C/min up to 300°C. A final, post-run time of 2 min at 300°C was maintained. Total run time was 13.55 min. Injector was set at 250°C and the He flow was set at 2.5 ml/min. Each OP or CA was identified based on the following criteria: retention time and 3-4 fragmentation ions with pre-defined relative amounts and 20% variability acceptance for each ion. Linearity was confirmed between 25 and 500 ng.g-1 with 5 point calibration curves and r2 >0.99. Recovery was determined between 76% and 104% for all spiked samples and repeatability was considered acceptable with coefficients of variation <15%.

#### **2.4.3 Pyrethroids pesticides analyses**

5 g of tissue (liver or muscle) sample was shaken in 60 ml ethanol and 10 g anhydrous sulfate, and then filtered trough a Whatman 1 PS membrane. Extract was dissolved in 5 ml methanol and underwent a second filtration procedure. Concentrations were determined by GC / ECD and confirmed by GC/MS according to a modified method of the French Food Safety Authority (Anses Met AFSSA). An Agilent GC-ECD 6850 with a 30m HP1 column (0.32 mm ID, 0.25µm film) was used. For each samples standard and spiked sample, 2 µL were injected. The temperature program was common to OCs', PCBs and pyrethroids (initial temp: 100°C, first ramp 6°C/min up to 220 °C held for 10 min, 2nd ramp 7 °C/min up to 285°C, held for 1 min, total run time 42.29 min) Injector was at 230°C, detector at 300°C. Total He flow was 9 ml/min. Pyrethroids were identified according to their retention times. Linearity was confirmed between 10 and 100 ng.g-1 with 5 point calibration curves and r2 >0.99. Recovery was determined between 82% and 94% for all spiked samples and repeatability was considered acceptable with coefficients of variation <15%. For all positive samples, a confirmatory analysis was performed with GC/MS in SIM mode. Identification was based on retention times and 3 or 4 ions.

#### **2.4.4 Herbicides analyses**

296 Pesticides in the Modern World - Risks and Benefits

2.0-8.0 g of tissue were sampled and 30 ml of hexane/acetone 75/25 mix was added. Each sample was blended twice with an Ultraturrax® (Ika, Werke, Germany) and filtered trough a phase separator membrane. The extract was evaporated at 60 °C in a rotary evaporator. The

Two ml of fuming sulphuric acid (SO3 7%) were added, and after centrifugation at 4x *g*, 1 ml of the supernatant was used for OC pesticides quantification by gas chromatography with electron capture detection material. Temperature program and injection conditions are described in Lemarchand et al. (2007; 2010). Each sample was run in duplicate. Organochlorine pesticides concentrations were calculated by using different mix standards. Recovery level on standard mixtures was always greater than 92%. All standards were purchased from CIL (St Foy la Grande, France), and purity was > 99%. Linearity was determined between 5 and 100 ng.g-1 (*r*² > 0.99 on standards and spiked samples, 5-point calibration curves). Limits of detection were between 0.5 and 1.0 ng.g-1 lipids for individual PCB congeners. Cod liver oil (BCR349) certified material was used as a regular quality

5 g of muscle sample was shaken with 60 ml dichloromethane and 10 g anhydrous sulfate. Mix was then filtered trough a Whatman 1 PS membrane, and evaporated under vacuum at 40°C. Dry samples were diluted in 3 ml ethanol, and underwent an ultrasonic step. Extract was then purified with a Sep pack R 300 (Silica Waters, 020810; 500 mg) column conditioned with 2 ml methanol and 2 ml ethanol. 2 ml dichloromethane were used for column elution. Purified samples were dried and diluted with 3 ml dichloromethane. Organophosphate (OP) and 2 carbamates (CA) pesticides (Dichlorvos, Carbofuran, Mevinphos, Phorate, Phorate oxon, Phorate sulfone, Methiocarbe, Terbufos, Diazinon, Disulfoton, Chlorpyriphos methyl, Chlorpyriphos ethyl, Fenitrothion, Pyrimiphos methyl, Malathion, Fenthion, Parathion, Methidathion, Disulfoton sulfone, Triazophos) concentrations were determined by GC/MS in SIM mode (OP + carbofuran and methiocarbe). A 5973N MS coupled with a 6890 GC (Agilent®) was used, with a 30m HP5-MS column (0.25 mm ID, 0.25µm thickness). For each samples standard and spiked sample, 2 µL were injected. The temperature program was 100°C (2 min), 55°C/min up to 200 °C (held for 5 min), 50 °C up to 220 °C (held for 3 minutes), followed by 60 °C/min up to 300°C. A final, post-run time of 2 min at 300°C was maintained. Total run time was 13.55 min. Injector was set at 250°C and the He flow was set at 2.5 ml/min. Each OP or CA was identified based on the following criteria: retention time and 3-4 fragmentation ions with pre-defined relative amounts and 20% variability acceptance for each ion. Linearity was confirmed between 25 and 500 ng.g-1 with 5 point calibration curves and r2 >0.99. Recovery was determined between 76% and 104% for all spiked samples and

repeatability was considered acceptable with coefficients of variation <15%.

5 g of tissue (liver or muscle) sample was shaken in 60 ml ethanol and 10 g anhydrous sulfate, and then filtered trough a Whatman 1 PS membrane. Extract was dissolved in 5 ml methanol and underwent a second filtration procedure. Concentrations were determined by GC / ECD and confirmed by GC/MS according to a modified method of the French Food Safety Authority (Anses Met AFSSA). An Agilent GC-ECD 6850 with a 30m HP1 column

**2.4 Pesticides quantification methods 2.4.1 Organochlorine pesticides**

dry extract was dissolved in 10 ml hexane.

**2.4.2 Organophosphate pesticides analyses**

**2.4.3 Pyrethroids pesticides analyses**

control.

2 g of muscle sample was shaken during 5 minutes in 8 ml acetone, and then centrifuged at 4x g; supernatant was placed in separate tubes, and this extraction was performed twice. Samples were evaporated under nitrogen, and dry extract was dissolved in 1 ml aceton/methanol (50:50) solution. Extract was then purified with a SPE C18 500 mg column conditioned with 2 ml acetone and 2 ml methanol. Column was vacuum dried and purified samples were diluted in 3 ml acetone. After drying under nitrogen, samples were diluted in 1 ml methanol. Herbicides (Trifluraline, Atrazine, Simazine, Terbuthylazine, Diuron, Alachlor, Metolachlor, Cyanazine, Epoxyconazloe) concentrations were determined by GC/MS spectrometry. A 5973N MS coupled with a 6890 GC (Agilent®) was used, with a 30m HP5-MS column (0.25 mm ID, 0.25µm thickness). For each samples standard and spiked sample, 2 µL were injected. The temperature program was 85°C held 1 min, followed by 6°C/min up to 170°C (held for 12 min), then followed by 20°C/min up to 280°C, held for 4.33 min (total run time 37 min). Injector was at 250°C and in the splitless mode. Each herbicide was identified based on the following criteria: retention time and 3-4 fragmentation ions with pre-defined relative amounts and 20% variability acceptance for each ion. Linearity was confirmed between 100 and 500 ng.g-1 with 5 point calibration curves and r2 >0.99. Recovery was determined between 67% and 98% for all spiked samples and repeatability was considered acceptable with coefficients of variation <15%.


**Table 1.** Descriptive items.

#### **5. Results**

#### *5.1. Preliminary Study*

In order to ensure that the collected data were suitable for all factor analysis, the Kaiser-Meyer-Olkin (KMO) test was carried out. The KMO test result was 0.867, greater than 0.8, and the Bartlett's test showed a satisfactory result when *p* < 0.000, so the samples passed the reliability test, and factor analysis could be carried out.

#### *5.2. Exploratory Study*

In this paper, SPSS 21.0 was used for exploratory factor analysis (EFA) to extract principal component factors. As shown in Table 2, nine factors were obtained through principal component analysis, and the cumulative explanatory variance of these factors was 82.7%. In addition, Cronbach's α coefficient was greater than 0.8, and the sample data were good, which shows that these nine factors can be used to explain all measured items.

**Table 2.** Results of the exploratory factor analysis.

**Table 2.** *Cont.*

Note: please see Table 3 for the meaning of the abbreviations in Table 2.

#### *5.3. Confirmatory Study*

Confirmatory factor analysis (CFA) was used to verify the model. It can be seen from Table 3 that standardized load coefficients were all greater than 0.8, and were significant when the confidence was greater than 95%, and R2 was greater than 0.5. Through the confirmatory factor analysis test, it showed that the measurement item structure was good and that the model could be accepted completely (χ2 = 216.144, df = 114, χ2/df = 1.896, RMSEA = 0.061, CFI = 0.939, GFI = 0.868, AGFI = 0.835, NFI = 0.880, TLI = 0.931).

**Table 3.** Parameter estimates of the confirmatory factor analysis.

**Table 3.** *Cont.*

The results show that the t-value of each item was higher than the critical value, the significance level was 0.05, and the R2 value of each variable was greater than 0.5. This is sufficient evidence of convergence efficiency. The discriminant validity was tested by comparing the mean variance (AVE) extracted and the square correlation between structures. The results show that the AVE was greater than 0.5, and the correlation coefficient between factors was less than AVE, indicating that there is good discrimination validity between factors (see Table 4).

**Table 4.** Discriminant validity.


**1. Introduction**

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242 Current Advances in Amyotrophic Lateral Sclerosis

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318-27.

Recently, eye-gaze input systems have been developed as novel human–machine interfaces [1-10]. Their operation requires only eye movements by the user. Based upon such systems, many communication aids have been developed for people with severe physical disabilities, such as amyotrophic lateral sclerosis (ALS). Eye-gaze input systems commonly employ noncontact eye-gaze detection for which an incandescent, fluorescent, or LED lamp can be used as the source of infrared or natural light. Detection based on infrared light can detect eye gaze with a high degree of accuracy [1-3] but requires an expensive device. Detection based on natural light uses ordinary devices and is therefore cost-effective [4,5]. However, an eye-gaze input system for natural light has a low degree of accuracy.

We have previously developed an eye-gaze input system for people with severe physical disabilities [8-10]. This system uses a personal computer (PC) and a home video camera to detect eye gaze under natural light. The camera (e.g., a DV camera) can easily be connected to a PC through an IEEE 1394 interface. The frames taken by the camera can be analyzed in real time using the DirectShow library by Microsoft. We developed image analysis software to detect eye gaze. Our eye-gaze input system runs the software on Windows. This system does not require any special devices and is easily customizable. Therefore, this system is not only cost-effective but also versatile. Moreover, it can be operated under natural light and thus is suitable for personal use.

© 2013 Kiyohiko et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **2. Current eye-gaze input systems**

Many systems or devices have been developed as communication aids for ALS patients. For example, the E-tran (eye transfer) frame is used for communication between ALS patients and others. The E-tran frame is a conventional device and its structure is very simple. It is a transparent plastic board with characters, such as the alphabet, printed on it. When using the E-tran frame, a communication partner (helper) holds it over the user's face. Specifically, the user gazes at the place where the character that the user wishes to communicate is positioned. The helper moves the E-tran frame until the eye gaze of the user corresponds with that of the helper. Therefore, the helper can determine the character from the user's eye gaze. A user who can gaze at the characters on the E-tran frame can also communicate with others. In addition, the E-tran frame does not require power supply and is therefore highly portable. However, considerable skill is required to use the E-tran frame.

Indicators

http://dx.doi.org/10.5772/56560

245

Eye-Gaze Input System Suitable for Use under Natural Light and Its Applications Toward a Support for ALS Patients

A B C D E

F G H I Next

F I Next

Eye gaze is defined as a unit vector in a three-dimensional coordinate space. The origin of this unit vector is the center of the eyeball. Generally, the user's gaze is detected on a two-dimen‐ sional plane. It has horizontal and vertical components. The method of tracking the iris (the colored part of the eye) is the most popular method for eye-gaze detection using image analysis in natural light [4-6]. For example, if the edge between the iris and the sclera (the white part of the eye) is estimated by image processing, the appropriately approximated ellipse of the edge shows the location of the iris. However, it is difficult to distinguish the iris and the sclera by image analysis, because the edge between the iris and the sclera is not sharp. In addition, if a large part of the iris is hidden by the upper and lower eyelids, the measurement errors increase, because the obscuring of the iris by the eyelids causes estimation errors in the delineation of the iris. To resolve these issues, we propose a new image analysis method for detecting eye gaze using both the horizontal and vertical directions. This detection method is based on the limbus tracking method. Our eye-gaze detection method can obtain the coordi‐

In our eye-gaze detection method, the video camera records images of the user's eye from a distant location (the distance between the user and camera is approximately 70 cm), and then this image is enlarged. The head movements of the user induce a large error in the detected gaze. We compensated for the head movements by tracing the location of a

The limbus tracking method is an eye-gaze detection method using the difference in reflectance between the iris and the sclera. By this method, eye gaze can be estimated with relative ease, and therefore it has been used since the 1960s [12]. The general eye-gaze detection system using the limbus tracking method irradiates an eyeball of a subject with infrared light. The eye gaze of the subject is detected by measuring the reflected light using optical sensors such as

A

**3. Eye-gaze detection by image analysis**

**Figure 1.** Overview of eye-gaze input

nates of the user's gaze point.

corner within the eye.

photodiodes.

**3.1. Horizontal gaze detection**

Input area

The row–column scanning system is also used to aid the communication of ALS patients. This system can be operated with one switch. In other words, the user can input characters or operate a PC by using their physical residual function. The row–column scanning system is configured to exploit simple hardware. For example, if the user employs the screen keyboard that is installed on Windows, the user can operate many of the Windows software applications. It takes considerable time to input using the row–column scanning system, because this system operates by scanning the rows and columns of keyboards using only one switch. To improve upon this situation, a new method for row–column scanning has been reported [11]. This method optimizes the speed of row-column scanning by using a Bayesian network for machine learning. However, a patient with severe ALS cannot use the row–column scanning system, despite its single switch.

Our eye-gaze input system mitigates these weaknesses. In a general eye-gaze input system, the icons displayed on the PC monitor are selected by the user gazing at them, as shown in Figure 1. These icons are called indicators and are assigned to characters or functions of the application program. The eye-gaze input has to detect the user's gaze in order to ascertain the selected indicator. Many eye-gaze detection methods have been developed in the past. Several systems use the EOG(electro-oculogram) method for eye-gaze detection [7], which detects eye gaze by the difference in the electrical potential between the cornea and the retina. It is a contact method that uses electrodes placed around the eye. Although cost-effective, some users find that long-term use of the electrodes is uncomfortable. Therefore, many systems detect eye gaze using non-contact methods [1-10]. Specifically, the user's gaze is detected by analyzing images of the eye (and its surrounding skin) captured by a video camera. To classify the many indicators, most conventional systems use special devices such as infrared light [1-3] or multiple cameras [6]. Nevertheless, in order to be suitable for personal use, the system should be inexpensive and user-friendly. Therefore, a simple system using a single camera in natural light is desirable [4-6]. However, natural-light systems often have low accuracy and are capable of classifying only a few indicators [4]. This makes it difficult for users to perform a task that requires many functions, such as text input. To solve these problems, a simple eye-gaze input system that can classify many indicators is needed.

**Figure 1.** Overview of eye-gaze input

**2. Current eye-gaze input systems**

244 Current Advances in Amyotrophic Lateral Sclerosis

considerable skill is required to use the E-tran frame.

system that can classify many indicators is needed.

despite its single switch.

Many systems or devices have been developed as communication aids for ALS patients. For example, the E-tran (eye transfer) frame is used for communication between ALS patients and others. The E-tran frame is a conventional device and its structure is very simple. It is a transparent plastic board with characters, such as the alphabet, printed on it. When using the E-tran frame, a communication partner (helper) holds it over the user's face. Specifically, the user gazes at the place where the character that the user wishes to communicate is positioned. The helper moves the E-tran frame until the eye gaze of the user corresponds with that of the helper. Therefore, the helper can determine the character from the user's eye gaze. A user who can gaze at the characters on the E-tran frame can also communicate with others. In addition, the E-tran frame does not require power supply and is therefore highly portable. However,

The row–column scanning system is also used to aid the communication of ALS patients. This system can be operated with one switch. In other words, the user can input characters or operate a PC by using their physical residual function. The row–column scanning system is configured to exploit simple hardware. For example, if the user employs the screen keyboard that is installed on Windows, the user can operate many of the Windows software applications. It takes considerable time to input using the row–column scanning system, because this system operates by scanning the rows and columns of keyboards using only one switch. To improve upon this situation, a new method for row–column scanning has been reported [11]. This method optimizes the speed of row-column scanning by using a Bayesian network for machine learning. However, a patient with severe ALS cannot use the row–column scanning system,

Our eye-gaze input system mitigates these weaknesses. In a general eye-gaze input system, the icons displayed on the PC monitor are selected by the user gazing at them, as shown in Figure 1. These icons are called indicators and are assigned to characters or functions of the application program. The eye-gaze input has to detect the user's gaze in order to ascertain the selected indicator. Many eye-gaze detection methods have been developed in the past. Several systems use the EOG(electro-oculogram) method for eye-gaze detection [7], which detects eye gaze by the difference in the electrical potential between the cornea and the retina. It is a contact method that uses electrodes placed around the eye. Although cost-effective, some users find that long-term use of the electrodes is uncomfortable. Therefore, many systems detect eye gaze using non-contact methods [1-10]. Specifically, the user's gaze is detected by analyzing images of the eye (and its surrounding skin) captured by a video camera. To classify the many indicators, most conventional systems use special devices such as infrared light [1-3] or multiple cameras [6]. Nevertheless, in order to be suitable for personal use, the system should be inexpensive and user-friendly. Therefore, a simple system using a single camera in natural light is desirable [4-6]. However, natural-light systems often have low accuracy and are capable of classifying only a few indicators [4]. This makes it difficult for users to perform a task that requires many functions, such as text input. To solve these problems, a simple eye-gaze input


*2.2. The Recycling Process of Concrete (Thermo-Mechanical Treatment According to PAT.229887)*

The initial crushing of previously disintegrated concrete specimens (100 × 100 × 100 mm) was carried out in a jaw crusher. The concrete was crushed to the grain size *d* ≤ 40 mm. In the next step, the concrete rubble was calcined in a ceramic laboratory furnace. Calcination of the recycled aggregate is necessary in order to dehydrate the cement paste. Dehydration of the cement paste reduces adhesion of the hardened mortar to the aggregate grains and eventually makes it easier to remove it from the surface of the aggregate. After thermal treatment and cooling, the recycled material was mechanically processed in a Los Angeles machine (Merazet, Poland). The grinding time was 15 min. This allowed for the final separation of the recycled cement mortar (RCM) from the surface of coarse aggregate grains. In the last step, the recycled material (fine and coarse fraction) was sieved in order to separate recycled cement mortar (<4 mm) from coarse aggregate (>4 mm). The recycled coarse aggregate and recycled cement mortar after the full process of recycling are shown in Figures 1 and 2. The coarse aggregate (>4 mm) obtained in recycling process can be used in new concrete as a substitute of natural aggregate. The test results of properties of concrete with recycled aggregate were presented in [8].

**Figure 1.** Recycled coarse aggregate obtained after thermal and mechanical treatment.

**Figure 2.** Recycled cement mortar (fine fraction) obtained after thermal and mechanical treatment, used to tests.


**2.1.2 Exposure to low temperature for one minute**

198 Atopic Dermatitis – Disease Etiology and Clinical Management

TRPV4 (Chung et al. 2003), both of which are activated by high temperature (around 30oC),

Fig. 1. Schematic diagram of skin (top) and photomicrograph illustrating the skin structures

We have shown that changes of calcium dynamics are associated with epidermal permeability barrier homeostasis [Denda 2003a]. TRPs are cation-permeable channels. Thus, we hypothesized that activation of TRPs might influence barrier homeostasis. To evaluate the influence of these receptors on barrier homeostasis, we incubated hairless mouse skin and human skin at various temperatures immediately after tape stripping

(bottom).

were also found to be expressed in keratinocytes.

Previous studies have identified cold-sensitive proteins, TRPA1 and TRPM8, that are activated by low temperature (<22oC) in peripheral nerve cells [Story 2003][Peier 2002b]. Recently, TRPA1 was also found in epidermal cells, in which it is activated by lower temperature (<17oC) [Atoyan 2009]. We demonstrated that exposure of cultured human keratinocytes to low temperature induced elevation of intracellular calcium [Tsutsumi 2010]. When the temperature of the medium was reduced to 17~22oC, elevation of intracellular calcium was observed. The extent of elevation was greater in nondifferentiated cells than in differentiated cells. Application of Ruthenium Red (a nonselective TRP blocker) and HC030031 (a specific antagonist of TRPA1) reduced the elevation. These results suggest that functional cold-sensitive calcium channels, TRPA1and/or TRPM8, are present in human epidermal keratinocytes. Thus, we hypothesized that modulation of TRPA1 and/or TRPM8 might influence epidermal permeability barrier homeostasis.

To test this idea, we first examined the effects of topical application of agonists of TRPA1 and brief cold exposure on the barrier recovery rate after barrier disruption [Denda 2010a]. Topical application of a TRPA1 agonist, allyl isothiocyanate or cinnamaldehyde, accelerated the barrier recovery after tape stripping. The effect of both agonists was blocked by HC030031, an antagonist of TRPA1. Brief exposure (1 minute) to cold (10-15oC) also accelerated barrier recovery and this acceleration was also blocked by HC030031. Electronmicroscopic studies indicated that brief cold exposure accelerated lamellar body secretion between stratum corneum and stratum granulosum, while pre-treatment with HC030031 inhibited the secretion. These results support the hypothesis that TRPA1 is associated with epidermal permeability barrier homeostasis.

We next examined the effect of topical application of TRPM8 modulators on epidermal permeability barrier homeostasis [Denda 2010b]. Immunohistochemical study and RT-PCR confirmed the expression of TRPM8 or TRPM8-like protein in epidermal keratinocytes. Topical application of TRPM8 agonists, menthol and WS 12, accelerated barrier recovery after tape stripping. The effect of WS12 was blocked by a non-selective TRP antagonist, Ruthenium Red, and a TRPM8-specific antagonist, BTCT. Topical application of WS12 also reduced epidermal proliferation associated with barrier disruption under low humidity, and this effect was blocked by BTCT. Our results indicate that TRPM8 or a closely related protein in epidermal keratinocytes plays a role in epidermal permeability barrier homeostasis and epidermal proliferation after barrier insult.

Physical and Chemical Factors that Improve Epidermal Permeability Barrier Homeostasis 201

function. Again, red light accelerated barrier recovery, while blue light delayed it. An electronmicroscopic study suggested that red light accelerated lamellar body secretion, while blue light blocked it. These results indicate that visible radiation affects skin barrier homeostasis.

Rhodopsin is a well-known photosensitive protein found in rod cells of the retina and detects light/dark contrast. Cone opsins are also photosensitive receptors in the cone cells of the retina and detect color. We have reported immunochemical studies using antirhodopsin and anti-opsin antibodies on human skin (Tsutsumi 2009). Both mouse retina and human epidermis showed clear immunoreactivity with each antibody. Interestingly, immunoreactivity against longer-wavelength opsin antibody was observed in the basal layer of the epidermis, while immunoreactivity against rhodopsin and shorterwavelength opsin was observed in the upper layer. PCR analysis confirmed the expression of rhodopsin-like and opsin-like genes in human retina and skin. These results suggest that a series of proteins, which play a crucial role in visual perception, are also

In retina, transducin and phosphodiesterase 6 play key roles in signal transmission. Thus, we hypothesized that these proteins might exist in epidermal keratinocytes and be associated with barrier homeostasis (Goto 2011). Immunohistochemical study and reverse transcription-PCR assays confirmed the expression of both transducin and phosphodiesterase 6 in epidermal keratinocytes. Topical application of 3-isobutyl-1 methylxanthine, a non-specific phosphodiesterase inhibitor, blocked the acceleration of barrier recovery by red light. Topical application of zaprinast, a specific inhibitor of phosphodiesterases 5 and 6, also blocked the acceleration, while T0156, a specific inhibitor of phosphodiesterase 5, had no effect. Red-light exposure reduced the epidermal hyperplasia induced by barrier disruption under low humidity, and the effect was blocked by pretreatment with zaprinast. Our results indicate phosphodiesterase 6 is involved in the recovery-accelerating effect of red light on the disrupted epidermal permeability barrier. Also, epidermal keratinocytes have a similar energy conversion

Fig. 3. Effects of visible radiation on epidermal permeability barrier homeostasis.

That is, epidermal keratinocytes might have a sensory system for visible radiation.

expressed in human epidermis.

system to that of the retina.

Fig. 2. Temperature ranges within which various TRP receptors are activated, and effect of their activation on epidermal permeability barrier homeostasis.