Second-order Derivative Spectra Research Articles

A comprehensive and innovative chemometric approach: Archaeometric analysis of the sherds from the Neolithic Period to the Chalcolithic and early Bronze Age with the full deployment of FTIR's molecular spectroscopic capabilities

In this study, the mineralogical composition of 284 sherds obtained from the sites of Yeşilova Höyük and Yassıtepe Höyük, two of the oldest settlements in Western Anatolia, and corresponding to a wide period starting from the Neolithic period to the Chalcolithic and Early Bronze Age (EBA), was studied by attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy. FTIR data was used to classify the sherds and state their firing conditions as a result of determining their mineralogical composition using principal component analysis (PCA), multivariate curve resolution (MCR), and hierarchical cluster analysis (HCA). According to the PCA results, the second-order derivative FTIR spectrum with 19 smoothing points in the range of 1300–400 cm−1 was determined to be the most successful model in classifying sherds. As a result of MCR, which allows comparison of standard minerals obtained within the scope of the study, it was determined that kaolinite, illite, chlorite, and some feldspar types were dominant in the sherds. According to HCA analysis, all sherds except five of them exhibited a similar mineralogical structure. According to the derivative spectra, it was seen that the sherds had a composition consisting of kaolinite, illite, hematite, chlorite, and some feldspar types at different rates. Upon examining the data, it becomes evident that the sherds are composed of nearly identical minerals and might have been fired in an oxidizing atmosphere. Even while 279 sherds had many mineralogical traits, N-061, EB-030, N-043, C-068, and N-059 were found to have distinct proportions of comparable minerals, including more kaolinite, illite, calcite, and chlorite. Regarding color, additives, and the amount of ceramic pastes present, these five sherds differ from the others. This suggests that there might have been attempts at manufacture or pots brought in from outside the Yeşilova Höyük-Yassıtepe Höyük sites. The results provided from the derivative spectra showed that all sherds except five of them have been fired at about at temperatures slightly above 450–500 °C due to their relatively low kaolinite character, but below 800 °C because diopside and similar high-temperature minerals could not be detected. The five sherds may have been fired at a temperature of around 450–500 °C due to the presence of a high character of kaolinite, illite, and chlorite. It was understood that the residents of the sites of Yeşilova Höyük and Yassıtepe Höyük produced their ceramics using the same raw materials and the same production methods from the Neolithic period to EBA thanks to FTIR and chemometrics, which are very good tools for analyzing large numbers of sherds with low cost and fast analysis time.

Color Genesis and Chromatography of Yellow Silicified Corals

Color plays a vital role in revealing the formation environment and metasomatic processes of silicified coral. This study investigated the color mechanism and colorimetric characteristics of yellow silicified coral from the aspects of gemology and colorimetry. A Mako G-507C industrial camera, Raman spectroscopy, UV-Vis, EDXRF, and XRD were used for the 16 samples in this study. The results showed that the yellow color of the silicified coral was produced by Fe3+ and influenced by its degree of crystallization. The Raman peaks of all silicified corals were consistent with the standard spectral group peaks of α-quartz, where the yellow part was inferred to be goethite. The peaks at 545 and 505 nm, with a secondary peak near 435 nm in the UV-vis first-order derivative spectrum, were consistent with the presence of hematite and goethite, respectively. The band positions of the second-order derivative spectrum were shown to belong to one single-electron leap 6A1 → (4E;4A1) and one electron pair leap (6A1 +6A1) → (4T1 +4T1). The chroma and lightness were mainly affected by Fe3+. By analyzing the correlation between the Fe content and the characteristic peaks, it was found that an increase in the Fe content led to a red shift in the peak position of the main characteristic peaks, as well as an increase in the hight of the corresponding peaks in the UV-visible first-order derivative spectra. In silicified corals, an increasing crystallinity index is correlated with a decreasing phase proportion of moganite, decreasing Fe content in the bulk, and low chroma.

High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis

Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control.

Improved discrimination of COVID-19 based on data enhancement technology and an information balance feature selection (INB) method

The coronavirus disease (COVID-19) ravaged the world in late 2019 and posed a serious threat to human life and property destruction on a global scale. In this paper, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) method was selected for balancing the data sample, and an information balance feature selection (INB) method was first proposed to realize the accurate discrimination of COVID-19 saliva samples based on the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. The results of the experiment showed that the INB method obtained higher classification accuracy than the traditional feature selection methods in both the original spectrum and the second-order derivative spectrum, especially in the second-order derivative spectrum where all the indexes reached about 85 %. In addition, the combination of WGAN_GP data augmentation and the INB method resulted in an accuracy of 88.7 % for the original spectrum and even 90.6 % for the second-order derivative spectrum. According to these findings, classification research using the WGAN_GP data enhancement model may increase classification accuracy. Additionally, the ability to successfully separate COVID-19 indicates that the INB method to identify spectral data features is a workable method, which also offers a fresh viewpoint on feature selection.

Comparative analysis of infrared and electrochemical fingerprints of different medicinal parts of Eucommia ulmoides Oliver

The method of establishing fingerprint profiles of chemical characteristics of herbal medicines is currently one of the most effective methods recognized internationally for controlling the quality of herbal or natural medicines. This work systematically examines geographical differences using the bark and leaves of Eucommia ulmoide as the object of study. Electrochemical fingerprinting and Fourier transform infrared (FTIR) fingerprinting were used to obtain information on different samples. We established a common pattern for the profiles. The results showed that the differences in response positions and intensities of the aspiration fingerprint profiles of the samples from various origins were more obvious and could effectively identify the origins. We combined this with principal component analysis, and the results showed that the differences in chemical composition and content of the samples were correlated with the geographical locations in which they were located. The comparative analysis of the second-order derivative spectra further illustrated the differences in chemical composition and content between samples of different origins.

Eco-friendly spectrophotometric methods for determination of remdesivir and favipiravir; the recently approved antivirals for COVID-19 treatment

Remdesivir (REM) and Favipiravir (FAV) are recently approved antivirals prescribed in severely ill COVID-19 patients. Therefore, development of new, simple, rapid, sensitive, and selective methods for analysis of such drugs in their pharmaceutical formulations will be highly advantageous. Herein, we have developed different spectrophotometric methods for analysis of the studied analytes. Method I is based on direct spectrophotometric analysis of REM and FAV in ethanol at λmax 244 and 323 nm, respectively. For simultaneous quantitation of REM and FAV, methods II-V were followed. Method II is based on derivative spectrophotometry in which REM was determined in second-order derivative spectra at 248 nm (the zero-crossing wavelength for FAV), while FAV was measured in first-order derivative spectra at 337 nm (the zero-crossing point for REM). Method III is the dual-wavelength method in which spectral intensities were subtracted at 244–207 nm for REM and at 330–400 nm for FAV. Method IV is the ratio subtraction in which ratio spectra were obtained by a suitable divisor followed by subtraction of intensities at 272–340 nm and 335–222 nm for REM and FAV, respectively. Method V is the derivative ratio method in which the obtained ratio spectra in method IV were converted to first-order derivative and then REM and FAV were recorded at 280 and 340 nm, respectively. Calibration graphs were linear in the ranges of 1–10 µg/mL for REM through all methods and 1–20 µg/mL for FAV in methods I and II, and 2–20 µg/mL by the other methods. The evolved methods were applied to pharmaceutical dosage forms of REM and FAV. All the proposed methods were further applied to human plasma samples containing both drugs with acceptable mean recoveries.

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging.

The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples.

Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR.

Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. This research aimed to identify the geographical traceability for different parts of W. cocos. The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm-1 , 6300-5150 cm-1 and 4450-4050 cm-1 of original spectra and the second-order derivative (SD) spectra preprocessed. From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

Structural, optical and dielectric properties of (Co and Sm) co-implanting O-polar ZnO films on sapphire substrate

In this paper, molecular beam epitaxy (MBE) was used to prepare O-polar ZnO films on the sapphire substrate. Co ions and Sm ions were respectively injected into the O-polar ZnO films by ion implantation, so that Co-implanted O-polar ZnO film, Sm-implanted O-polar ZnO film, and (Co and Sm) co-implanted O-polar ZnO film were prepared. We adopted X-ray diffraction (XRD), Atomic Force Microscope (AFM), Raman spectroscopy, photoluminescence spectra (PL), absorption spectroscopy and Spectroscopic Ellipsometry (SE) to study the structure, optical and dielectric properties of all samples. XRD showed that Co atoms and Sm atoms had entered the crystal lattice and the high-quality ZnO films had been prepared. Compared with the unimplanted sample, the surfaces of the implanted samples were granular and the roughness became larger. The co-implanted sample had the smaller roughness than the single-implanted samples. It was found from Raman spectra that two modes related to the LO vibration mode appeared near 50–230 cm−1 and 520–570 cm−1 after implantation. The PL spectra indicated that the intensity of the Sm-implanted sample was greater than the (Co and Sm) co-implanted sample. After ion implantation, the results measured by SE showed the band gap values of the (Co and Sm) co-implanted sample was reduced, and a transition peak around 3.15 eV appears in the second-order derivative spectra of the imaginary part ε2 of the dielectric function. These paved the way for their application and development in optoelectronic devices.

Saliva-based linezolid monitoring on a mobile UV spectrophotometer.

In TB, therapeutic drug monitoring (TDM) is recommended for linezolid; however, implementation is challenging in endemic settings. Non-invasive saliva sampling using a mobile assay would increase the feasibility of TDM. To validate a linezolid saliva assay using a mobile UV spectrophotometer. The saliva assay was developed using NanoPhotometer NP80® and linezolid concentrations were quantified using second-order derivative spectroscopy. Sample preparation involved liquid-liquid extraction of saliva, using saturated sodium chloride and ethyl acetate at 1:1:3 (v/v/v). The assay was validated for accuracy, precision, selectivity, specificity, carry-over, matrix effect, stability and filters. Acceptance criteria were bias and coefficient of variation (CV) <15% for quality control (QC) samples and <20% for the lower limit of quantification (LLOQ). Linezolid concentrations correlated with the amplitude between 250 and 270 nm on the second-order derivative spectra. The linezolid calibration curve was linear over the range of 3.0 to 25 mg/L (R2 = 0.99) and the LLOQ was 3.0 mg/L. Accuracy and precision were demonstrated with bias of -7.5% to 2.7% and CV ≤5.6%. The assay met the criteria for selectivity, matrix effect, carry-over, stability (tested up to 3 days) and use of filters (0.22 μM Millex®-GV and Millex®-GP). Specificity was tested with potential co-medications. Interferences from pyrazinamide, levofloxacin, moxifloxacin, rifampicin, abacavir, acetaminophen and trimethoprim were noted; however, with minimal clinical implications on linezolid dosing. We validated a UV spectrophotometric assay using non-invasive saliva sampling for linezolid. The next step is to demonstrate clinical feasibility and value to facilitate programmatic implementation of TDM.

Development and Validation of Rapid RP-HPLC and Green Second-Derivative UV Spectroscopic Methods for Simultaneous Quantification of Metformin and Remogliflozin in Formulation Using Experimental Design

Recently, a new formulation containing metformin HCl (MFH) and remogliflozin etabonate (RGE) has been approved for the management of diabetes mellitus. However, only one analytical method has been reported for the simultaneous determination of both the analytes. Therefore, the current study was designed to develop simple UV derivative spectroscopic and rapid RP-HPLC methods for simultaneous determination of MFH and RGE. The chromatographic separation of MFH and RGE was performed using a monolithic C18 column with an optimized chromatographic conditions carried out by full factorial Box–Behnken design model. The spectroscopic technique was based on the determination of peak amplitude of second-order derivative UV spectra at zero crossings. Further, both the methods were validated and compared statistically using Student’s-t-test and F-test, and employed for the concurrent estimation of MFH and RGE in laboratory mixed solutions and formulations. Perturbation plots and response surface models showed the effect of chromatographic parameters and the final chromatographic condition was selected from 47 solutions suggested by the desirability function. Further, UV spectroscopic and HPLC procedures showed good linearity in the range of 1–24 µg/mL and 2–150 µg/mL for RGE and 2–30 µg/mL and 5–200 µg/mL for MFH, respectively. The average percent assay was found to be 99.51% and 99.80% for MFH and 99.60% and 100.07% for RGE by spectroscopic and HPLC methods, respectively. The proposed methods were simple, accurate, precise, and rapid. Therefore, they can be used for regular quality control of MFH and RGE formulations and dissolution studies as well.

Method Transfer Evaluation for Digital Derivative Spectrophotometry Through its Resolution Parameter Comparison of Different Computer Programs.

The application assessment of different programs was performed with equivalence tests for method transfer pro second-order derivative spectrophotometry. The digital second-order derivative spectra were calculated on different instruments; GBC Scientific Equipment Cintra 20 (Cintral v.2.6 and Spectral v.1.70 software programs) and Thermo Scientific Evolution 300 (VISIONPro software) were analyzed using the amplitude A/B ratio (A = 2D265,263; B = 2D263,261). Amplitude A/B ratio is the resolution parameter for derivative spectrophotometry prescribed in European Pharmacopoeia. The obtained values for A/B ratio were either very similar or significantly different among programs: 0.669 (Cintral v.2.6), 0.549 (Spectral v.1.70), 0.556 (medium indirect VISIONPro), 0.557 (one-step Savitzky-Golay 7 VISIONPro), 0.689 (two-step Savitzky-Golay 7 VISIONPro). Method transfer was possible between Spectral v.1.70 and VISIONPro (medium indirect and one-step Savitzky-Golay 7), but the values obtained in Cintral v.2.6 were not comparable to the other programs. The absorbance data exported from both instruments were additionally calculated in OriginPro8 which provided almost the same mean A/B values (0.627 Cintral v.2.6; 0.624 VISIONPro), confirming that the two instruments recorded the same zero-order spectra. The calculation of resolution parameter could be used for verification of program comparison, which would enable transfer between sender and receiver laboratory. The accordance between program algorithms was confirmed when acceptable differences for values of resolution parameter (A/B ratios) were achieved.

Terahertz differential absorption spectroscopy using multifurcated subnanosecond microchip laser

Using spectral multifurcated oscillations in a passively Q-switched microchip laser, we demonstrate frequency-domain differential absorption spectroscopy in the terahertz (THz) frequency region. Within a single quasi-continuous-wave (QCW) excitation, a microchip laser comprising a 7-mm-long Nd:YAG/Cr:YAG composite ceramic provides up to three subnanosecond pulses with a 1064-nm wavelength and a 50-Hz QCW repetition rate. We have observed that the longitudinal mode of double and triple pulses shows stable bifurcation and trifurcation, respectively, induced by a spatial hole burning effect within the laser cavity. These pulses are directly used to drive an injection-seeded THz-wave parametric generator based on a MgO-doped LiNbO3 crystal, thereby generating up to three monochromatic, self-frequency-switched THz-wave pulses separated from each other in frequency by a free spectral range of the laser cavity. By precisely tuning one of the THz-wave frequencies to the gas absorption line, multifurcated THz-wave pulses facilitate the measurement of differential absorption signals every 20 ms without any active frequency modulation. We also show that first- and second-order derivative spectra of gas absorption can be derived from a single frequency sweep of multifurcated pulses without a reference spectrum and computational derivation. Our approach paves the way toward realization of a THz differential absorption lidar for use in fast gas sensing applications.

Estimation of copper concentration of rocks using hyperspectral technology

Rock geochemical information is important for mineral exploration and provides a theoretical basis for the rapid delineation of hidden minerals. Remote sensing technology provides the possibility of rapid and large-scale extraction of geochemical information from the earth’s surface. This study analyzed the relationship between copper concentration and rock spectra by first collecting 222 rock samples, and then measuring the copper concentration of rock samples in the laboratory and reflectance spectra using an ASD FieldSpec3 portable spectrometer. It finally established quantitative relationships between the original spectra, first-order derivative spectra and second-order derivative spectra and copper concentration, respectively, using the partial least squares support vector machine method (PLS-SVM). The results show that 1) The estimation accuracy of using second-order derivatives spectra as input parameters to establish a model for estimating copper concentration is the highest, and the determined coefficient (R2) between the predicted value and real value reaches 0.54. 2) When the copper concentration is less than 80 mg/kg, the inversion model of copper concentration established using PLS-SVM obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.70248. When the copper concentration is greater than 80 mg/kg, the inversion model of copper concentration established using partial least squares (PLS) obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.49. The R2 between real copper concentration and copper predicted by the method of piecewise separate modeling reaches 0.816. Therefore, the method of segmental modeling has great potential to improve the accuracy of copper concentration inversion.

Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging.

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

ZnCdSe-CdTe quantum dots: A "turn-off" fluorescent probe for the detection of multiple adulterants in an herbal honey.

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.

Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.

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In this work, wool samples a) degreased and stored in desiccator (WD), b) degreased and stored freely (WF), c) cleaned in water and stored freely (WW) were irradiated by accelerated electron beam with doses within the range (0-400) kGy in air. Content of S-sulphonate as primary oxidation product was determined in WD using FTIR spectra inverted in the second-order derivative spectra. The uptake of humidity by all the samples at room temperature and 97% relative humidity was examined gravimetrically. As expected, the highest humidity uptake was observed for WD and the smallest one for WW. Development of the humidity uptake showed some fluctuation for all samples and, for WD the fluctuation corresponded with generated S-sulphonate, presumably due to formation of numerous H-bonds. The increasing uptake of humidity for WD and WW was observed up to 40 kGy dose while for WF, the uptake decreased below the initial level already from 16 kGy. Surface tension was measured using sink-float method combining stalagmometry. The initial surface tension of WW was higher than for WD and WF but, grew equally for all samples from 25 kGy. From 100 kGy dose the surface tension showed stabilized level. The humidity release by WD was measured using thermogravimetry. Variation of the rate humidity release around 100 °C, adequate for desorption of weakly bound water, and the mass residue of the heated wool for 120 °C corresponding to release of total water, showed an inverse dependence. It was concluded that content of S-sulphonate is responsible for variability of the sorption properties. The more S-sulphonate formed, the higher uptake of humidity and the lower rate of humidity release that can be observed. It is suggested that, within a certain range, a properly chosen dose can affect humidity uptake.

Enhanced Specificity for Detection of Frauds by Fusion of Multi-class and One-Class Partial Least Squares Discriminant Analysis: Geographical Origins of Chinese Shiitake Mushroom

Both multi-class and one-class discrimination analyses (DAs) have been widely used for tracing the geographical origins of Protected Designation of Origin (PDO) foods. However, due to the complexity of potential non-PDO frauds, both methods have encountered some problems. Because multi-class DA tries to classify two or more predefined classes, its classification results will be unreliable when it is used to predict a new object from an untrained class. One-class DA is developed using only the information concerning one-class objects, so they cannot necessarily ensure the model specificity for detection of various food frauds. In this work, a new chemometric strategy was proposed by fusion of multi-class and one-class DA to trace the geographical origin of a Chinese dried shiitake mushroom with PDO. The PDO shiitake objects (n = 161) and non-PDO objects (n = 264) from five other main producing areas were analyzed using near-infrared spectroscopy. The classification performance of multi-class DA, one-class DA, and model fusion was compared. With second-order derivative (D2) spectra, model fusion obtained a high sensitivity (0.944) and specificity (0.968). Model comparison indicates that fusion of multi-class and one-class DA can enhance the specificity for detecting various non-PDO foods with little loss of model sensitivity.