Peaberry coffee discrimination using UV-visible spectroscopy combined with SIMCA and PLS-DA

Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%). The overall results demonstrate that this method has excellent potential for discriminating grape quality.

Traditional analytical methods applied to the measurement of grape maturity and quality index in order to assess optimal harvest time have been proved to be slow and destructive. Therefore, non-destructive analytical techniques, including spectroscopy, can be a valid support for the choice of the best time to harvest. This study evaluated the feasibility of using a visible and near infrared spectral scanner (v. 1.4; DV Srl, Padova, Italy) with a detector in the region between 400-1000 nm to discriminate between grapes harvested at different times. Twelve clusters were harvested at 5 different times between October and December 2011. Spectra were acquired with a Spectral scanner on 3 intact berries from each bunch. These were randomly selected from top, medium and bottom zones, for a total of 180 spectra. Classification models were construed comparing 2 methods: soft independent modelling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The SIMCA model was developed building individual principal component analysis (PCA) models for the spectra of each harvest time. Different pre-treatment methods were tested in order to enhance the power of the model, thus enhancing the score differences among samples from different harvest times. The transformation that allowed the best statistical separation among scores of grapes from different harvest times was the second derivate of Norris. Therefore, the PCA model obtained from the spectra subjected to this pre-treatment was used for SIMCA classification. The PLS-DA model were developed applying the PLS2 algorithm. In order to construct discriminant models to classify bunch spectra according to the 5 harvest times, spectral variations were correlated with the 5 categories established. No pretreatments were previously applied in this last case since they did not improve the final result. The SIMCA method was unable to correctly classify grapes from harvest time 2 (59% of correct classification) and was less efficient compared to the PLS-DA model. Using the PLS-DA model, all the grapes were correctly classified (100%) with the exception of those from harvest time 5 (94%). The overall results demonstrate that this method has excellent potential for discriminating grape quality.

We report soft independent modeling of class analogy (SIMCA) analysis of laser-induced plasma emission spectra of edible salts from 12 different geographical origins for their classification model. The spectra were recorded by using a simple laser-induced breakdown spectroscopy (LIBS) device. Each class was modeled by principal component analysis (PCA) of the LIBS spectra. For the classification of a separate test data set, the SIMCA model showed 97% accuracy in classification. An additional insight could be obtained by comparing the SIMCA classification result with that of partial least squares discriminant analysis (PLS-DA). Different from SIMCA, the PLS-DA classification accuracy seems to be sensitive to addition of new sample classes to the whole data set. This indicates that the individual modeling approach (SIMCA) can be an alternative to global modeling (PLS-DA), particularly for the classification problems with a relatively large number of sample classes.

Hyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated. Three different sample sets containing healthy and infected oilseed rape leaves were acquired under different imaging acquisition parameters. Four discriminant models were built using full spectra, including partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and k-nearest neighbors (KNN). PLS-DA and SVM models were also built with the optimal wavelengths selected by principal component analysis (PCA) loadings, second derivative spectra, competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA). The optimal wavelengths selected for each sample set by different methods were different; however, the optimal wavelengths selected by PCA loadings and second derivative spectra showed similarity between different sample sets. Direct standardization (DS) was successfully applied to reduce spectral differences among different sample sets. Overall, the results demonstrated that using hyperspectral imaging with chemometrics for plant disease detection can be efficient and will also help in the selection of optimal variable selection, machine learning, and calibration transfer methods for fast and accurate plant disease detection.

Evaluation of adulteration in soy-based beverages by water addition using chemometrics applied to ATR-FTIR spectroscopy

A chromatographic procedure (HPLC-DAD) using a relatively rapid gradient has been combined with a chemometric curve deconvolution method, multivariate curve resolution-alternating least squares (MCR-ALS), in order to quantify caffeine and chlorogenic acid in green coffee beans. Despite that the HPLC analysis (at these specific operating conditions) presents some coeluting peaks, MCR-ALS allowed their resolution and, consequently, the creation of a calibration curve to be used for the quantification of the analytes of interest; this procedure led to a high accuracy in the quantification of caffeine and chlorogenic acid present in the samples. In a second part of this study, the possibility of classifying the green coffee beans on the basis of their cultivar (Arabica or Robusta), by partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), has been explored. SIMCA resulted in 100% of sensitivity and specificity for the Arabica class, while for the Robusta, it reached 66.7% of sensitivity and 100% of specificity, or 100% of sensitivity and 100% of specificity, depending on the extraction procedure followed prior to the chromatographic analysis; PLS-DA achieved 100% of correct classification independently of the procedure used for the extraction.

Discrimination of Brazilian artisanal and inspected pork sausages: Application of unsupervised, linear and non-linear supervised chemometric methods

Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

Counterfeit medicines are a global threat to public health. High amounts enter the European market, enforcing the need for simple techniques to help customs detect these pharmaceuticals. This study focused on physical profiling and IR spectroscopy to obtain a prime discrimination between genuine and illegal Viagra® and Cialis® medicines. Five post-tableting characteristics were explored: colour, mass, long length, short length, and thickness. Hypothesis testing showed that most illegal samples (between 60 and 100%) significantly differ from the genuine medicines, in particular for mass and long length. Classification and Regression Trees (CART) analysis resulted in a good discrimination between genuine and illegal medicines (98.93% correct classification rate for Viagra®, 99.42% for Cialis®). Moreover, CART confirmed the observation that mass and long length are the key physical characteristics which determine the observed discrimination. IR analysis was performed on tablets without blister and on tablets in intact blister. These data were analyzed using Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares - Discriminant Analysis (PLS-DA). Supervised techniques needed to be applied since Principal Component Analysis (PCA) was not able to generate the desired discrimination. Our study shows that a perfect discrimination between genuine and illegal medicines can be made by both SIMCA and PLS-DA without removing the tablets from the blister. This approach has the advantage of keeping the blister intact. Our study demonstrates that these user friendly techniques are reliable methods to aid customs to obtain a prime distinction between genuine and illegal samples on the spot. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACTA rapid and sensitive method for classification of virgin and recycled expanded polystyrene (EPS) food containers was developed using Fourier transform infrared spectroscopy (FTIR) and chemometrics. This method includes preparing a transparent film by dissolution, examining by FTIR and developing classification models. The degradation of EPS containers occurring during the recycling process was reflected by the carbonyl region of the infrared spectrum which was used as variables for multivariate data analysis. PCA was used to reduce the data dimension and view the sample similarities. Soft independent modelling of class analogy (SIMCA), partial least squares-discrimination analysis (PLS-DA) and linear discrimination analysis (LDA) were applied to construct three classification models. The best discrimination results were obtained by an LDA model, with all samples correctly classified. PLS-DA and SIMCA could not classify the recycled EPS samples with low levels of adulteration. When applying this method to commercially available EPS containers, about 45% of samples were shown to contain recycled polystyrene resins. It is concluded that the carbonyl region of the infrared spectra coupled with chemometrics could be a powerful tool for the classification of virgin and recycled EPS food containers.

Characterizing and classifying molecular variations within biological samples are critical for determining the fundamental mechanisms of biological processes. Toward these ends, time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was used to examine increasingly complex samples of biological relevance. The large, multivariate datasets were analyzed using five common statistical and chemometric techniques: principal component analysis (PCA), linear discriminant analysis (LDA), partial least‐squares discriminant analysis (PLSDA), soft independent modeling of class analogy (SIMCA), and decision‐tree analysis by recursive partitioning. PCA was found to provide insight into both the relative groupings of samples and the molecular basis for those groupings. For monosaccharide, pure protein, and complex protein mixture samples, LDA, PLSDA, and SIMCA all produced excellent classification. For mouse embryo tissues, however, SIMCA did not classify samples as accurately. The decision‐tree analysis was the least successful for all tested samples, providing neither as accurate a classification nor chemical insight. Based on these results we conclude that as the complexity of samples increases, so must the sophistication of the multivariate technique used to classify the samples. PCA is a preferred first step for understanding ToF‐SIMS data that can be followed by either LDA or PLSDA for effective classification. This study demonstrates the strength of the combination of ToF‐SIMS and multivariate analysis to classify increasingly complex biological samples. Applying these techniques to information‐rich mass spectral data opens the possibilities for new applications including classification of subtly different biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis. Copyright © 2008 John Wiley & Sons, Ltd.

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples (n=150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70 degrees Brix) and their spectra (2500-12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800-11 500 nm) rather than the whole spectrum (2500-12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial

White peach is a famous peach variety for its super-quality and high economic benefit. It is originally planted in Yuandong Villiage, Jinhua County, Zhejiang province. By now, it has been planted in many other places in southeast of China. However, peaches from different planting areas have dissimilar quality and taste, which result in different selling price. The objective of this research was to discriminate peaches from different planting areas by using near-infrared (NIR) spectra and chemometrics methods. Diffuse reflectance spectra were collected by a fiber spectrometer in the range of 800-2500 nm. Discriminant analysis (DA), soft independent modeling of class analogy (SIMCA), and discriminant partial least square regression (DPLS) methods were employed to classify the peaches from three planting areas 'Jinhua', 'Wuyi', and 'Yongkang' of Zhejiang province. 360 samples were used in this study, 120 samples per planting area. The classifying correctness were above 92% for both DA and SIMCA mdoels. And the result of DPLS model was slightly better. By using DPLS method, two 'Jinhua' peaches, three 'Wuyi' peaches, and three 'Yongkang' peaches were misclassified, the accruacy was above 95%. The results of this study indicate that the three chemometrics methods DA, SIMCA, and DPLS are effective for discriminating peaches from different planting areas based on NIR spectroscopy.

Evaluation of single and multi-feedstock biodiesel – diesel blends using GCMS and chemometric methods