Soft Independent Modeling Of Class Analogy Research Articles

Rapid Detection of Avocado Oil Adulteration Using Low-Field Nuclear Magnetic Resonance.

Avocado oil (AO) has been found to be adulterated by low-price oil in the market, calling for an efficient method to detect the authenticity of AO. In this work, a rapid and nondestructive method was developed to detect adulterated AO based on low-field nuclear magnetic resonance (LF-NMR, 43 MHz) detection and chemometrics analysis. PCA analysis revealed that the relaxation components area (S23) and relative contribution (P22 and P23) were crucial LF-NMR parameters to distinguish AO from AO adulterated by soybean oil (SO), corn oil (CO) or rapeseed oil (RO). A Soft Independent Modelling of Class Analogy (SIMCA) model was established to identify the types of adulterated oils with a high calibration (0.98) and validation accuracy (0.93). Compared with partial least squares regression (PLSR) models, the support vector regression (SVR) model showed better prediction performance to calculate the adulteration levels when AO was adulterated by SO, CO and RO, with high square correlation coefficient of calibration (R2C > 0.98) and low root mean square error of calibration (RMSEC < 0.04) as well as root mean square error of prediction (RMSEP < 0.09) values. Compared with SO- and CO-adulterated AO, RO-adulterated AO was more difficult to detect due to the greatest similarity in fatty acids’ composition being between AO and RO, which is characterized by the high level of monounsaturated fatty acids and viscosity. This study could provide an effective method for detecting the authenticity of AO.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

Information theory and machine learning based authentication of flaxseed oil using portable and handheld vibrational spectroscopy sensors

Flaxseed oil is one of the popular edible oils, and its adulteration has become a concern because of its high commodity value. Therefore, there is a need to detect adulterated flaxseeds with a rapid, non-invasive, and simple method to keep the quality of flaxseed oil. This study aimed to develop a machine learning algorithm and information theory to detect adulterated flaxseed oils by vibrational spectroscopy units combined with chemometrics. Oil spectra were collected with portable/handheld vibrational spectroscopic units and analyzed using conditional entropy, support vector machines (SVM), and soft independent modeling of class analogy (SIMCA) to generate classification models to identify the pureness of oils and partial least square regression (PLSR) to predict the adulterant levels and fatty acid distributions. Conditional entropy showed that two or three wavenumbers/shifts could discriminate the oils using machine learning algorithms with an accuracy of over 95%. SIMCA models determined the oils with an interclass distance (ICD) over 1.1. Specifically, the Fourier transform mid-infrared (FT-MIR) unit provided the best classification of the oils with ICD over 4.9. All instruments showed good performance in predicting fatty acids and adulteration levels with r val ≥0.89 and standard error prediction (SEP) ≤1.7%. Overall, these portable/handheld units showed great potential for rapid, simple, and non-invasive monitoring to identify adulterated flaxseed oils.

Adulteration detection of Sudan Red and metanil yellow in turmeric powder by NIR spectroscopy and chemometrics: The role of preprocessing methods in analysis

Turmeric powder is valued both for its medicinal properties and for its popular culinary use, such as being a component of curry powder. Due to its high demand in international trade, turmeric powder has been subject to economically driven and hazardous chemical adulteration. The objective of this study was to classify and quantify two major adulterants (Sudan Red and Metanil yellow) in turmeric powder using near-infrared (NIR) spectroscopy associated with chemometric models. The absorbance spectra of samples were obtained in frequency regions of 10,000–4200 cm−1. A Soft Independent Modeling of Class Analogy (SIMCA) model was established to determine the authenticity of the turmeric powder. A partial least squares (PLS) regression was used to predict the adulteration level. The different combinations of preprocessing methods such as standard normal variate transformation (SNV), median filter, Savitzky-Golay, wavelet, multiplicative scatter correction (MSC), first derivative (D1) and second derivative (D2) were applied to preprocess the spectral data, in which SNV combined with median filter and D1 performed best. The sample correct classification rate in SIMCA model were found 96.7 and 93.4 in both calibration and validation set respectively. The PLS regression models were evaluated by the coefficient of determination (r2) and root mean square error (RMSE). All PLS regression models established with the combination of different spectral pretreatment except of MSC, Savitzky-Golay, D1 and MSC, Wavelet, D1 showed low values of RMSE and ideal coefficient of determination for both calibration and prediction sets while using relatively few factors. Also, the PLS regression results showed that first derivative led to better calibration models when compared with the second derivative. The obtained statistical parameters for Sudan Red were r2 = 0.90, RMSE = 0.059 in test set and r2 = 0.91 and RMSE = 0.058 for metanil yellow in test set.

Secondary-metabolites fingerprinting of Argania spinosa kernels using liquid chromatography–mass spectrometry and chemometrics, for metabolite identification and quantification as well as for geographic classification

Argan (Argania spinosa L.) fruit kernels’ composition has been poorly studied and received less research intensity than the resulting Argan oil. The Moroccan Argan kernels contain a wealth of metabolites and can be investigated for nutritional and health aspects as well as for economic benefits. Ultra-Performance Liquid Chromatography Mass Spectrometry (UPLC-MS) was employed to trace the geographical origin of Argan kernels based on secondary-metabolite profiles. One-hundred and twenty Argan fruit kernels from five regions (‘Agadir’, ‘Ait-Baha’ ‘Essaouira’, ‘Tiznit’ and ‘Taroudant’) were studied. Characterization and quantification of 36 secondary metabolites (33 polyphenolic and 3 non-phenolic) were achieved. Those metabolites are highly influenced by the geographic origin. Then, the untargeted UPLC-MS fingerprint was decomposed by metabolomic data handling tools, such as multivariate curve resolution alternating least squares (MCR–ALS) and XCMS. The two resulting data matrices were pretreated and prepared separately by chemometric tools and then two data fusion strategies (low- and mid-levels) were applied on them. The four data sets were comparatively investigated. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Soft Independent Modeling of Class Analogies (SIMCA) were used to classify samples. The exploration or classification models demonstrated a good ability to discriminate and classify the samples in the geographical-origin based classes.Summarized, the developed fingerprints and their metabolomics-based data handling successfully allowed geographical traceability evaluation of Moroccan Argan kernels.

Rapid and non-destructive spatially offset Raman spectroscopic analysis of packaged margarines and fat-spread products

Spatially offset Raman spectroscopy (SORS) is a novel technique capable of measuring samples through the original packaging and recovering the spectra without the contribution of surface layers. Here, a portable SORS equipment was used to measure 62 samples of margarines and fat spreads through the original plastic container. Chemometric tools were used to analyse the data obtained. A total of 25 classification models were developed based on: (i) geographical origin, (ii) vegetable oils and (iii) some significant minor constituents present in the samples. Partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and soft independent modelling of class analogy (SIMCA) were used for model classification. Quantitative analysis using the partial least squares regression (PLSR) method was also performed to determine the total fat content. In parallel, a benchtop conventional Raman spectrometer was used to analyse the same samples, develop the models with the same training and validation sets in order to compare the results. The calculated classification performance metrics showed better classification models from SORS data than conventional Raman spectroscopy (CRS), highlighting the one-class SIMCA models for margarines containing phytosterols, olive oil or linseed oil. These models exhibited very high predictability (performance parameters with values equal to or higuer than 0.8, 0.9 and 1, respectively). The quantitation model developed from SORS exhibited a higher R2 than from CRS data, and prediction errors below 5% from SORS versus errors between 5 and 13% from CRS data.These results reveal the ability of SORS to avoid the influence of fluorescence, a major drawback when analysing Raman spectra, but also the potential of the technique as a fast, non-destructive and non-invasive analytical technique in the field of food analysis. In conclusion, the tandem 'SORS-chemometrics' has been shown to be a potential tool in the food quality and food authentication fields. Thus, it is necessary to perform further investigations in this field in order to advance the knowledge of this technique and to be able to develop new methods of rapid analysis.

NMR spectroscopy and chemometric models to detect a specific non-porcine ruminant contaminant in pharmaceutical heparin

Heparin has been used successfully as a clinical antithrombotic for almost one century. Its isolation from animal sources (mostly porcine intestinal mucosa) involves multistep purification processes starting from the slaughterhouse (as mucosa) to the pharmaceutical plant (as the API). This complex supply chain increases the risk of contamination and adulteration, mainly with non-porcine ruminant material. The structural similarity of heparins from different origins, the natural variability of the heparin within samples from each source as well as the structural changes induced by manufacturing processes, require increasingly sophisticated methods capable of detecting low levels of contamination. The application of suitable multivariate classification approaches on API 1H NMRspectra serve as rapid and reliable tools for product authentication and the detection of contaminants. Soft Independent Modeling of Class Analogies (SIMCA), Discriminant Analysis (DA), Partial Least Square Discriminant Analysis (PLS-DA) and local classification methods (kNN, BNN and N3) were tested on about one hundred certified heparin samples produced by 14 different manufacturers revealing that Partial Least Squares Discriminant Analysis (PLS-DA) provided the best discrimination of contaminated batches, with a balanced accuracy of 97%.

Fluorescence spectroscopy in tandem with chemometric tools applied to milk quality control

Fluorescence spectroscopy with chemometric tools was used to check the milk authentication against the fraudulent addition of whey. Different commercial kinds of milk were adulterated with whey at five different levels. The fluorescence spectra of the adulterated samples showed different emission profiles when excited at the wavelength associated with tryptophan residues (295 nm). The addition of whey caused a reduction in fluorescence intensity. Nevertheless, visual inspection was not enough to discriminate between adulterations or milk types. Initial principal component analysis (PCA) explained 98.21% of the variance, PC1 (96.23%) enabled discrimination between genuine and adulterated milk, and PC2 (1.98%) discriminated by fat content in milk. However, supervised methods allowed for more efficient performances. Among the tested techniques, partial least squares-discriminant analysis (PLS-DA) allowed better classification than most evaluated approaches to soft independent modeling of class analogy (SIMCA). Overall, PLS-DA also showed better accuracy than SIMCA for classifying genuine and adulterated milk, with 100% predictive accuracy, sensitivity and specificity for whole and skim milk. Despite demonstrating suitable classification (>80%) of genuine and adulterated milk, it was not possible to classify the samples according to the level of adulteration.

Hierarchical classification and matching of mid-infrared spectra of paint samples for forensic applications

A novel fast and automatic methodology for the hierarchical classification and similarity matching of mid-infrared spectra of paint samples based on the principles of Soft Independent Modelling of Class Analogy (SIMCA) and on the definition and properties of the Mahalanobis distance is here proposed. This approach was tested in a so-called market study (i.e., targeting products largely accessible to the general public and conceived for a considerably wide range of usages) conducted across the surroundings of the city of Lille, in France, and has permitted not only to successfully achieve the chemical characterisation of most of the analysed samples but also to discover specific commonality patterns among specimens sharing the same chemical features.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self.

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Evaluation of Portable Vibrational Spectroscopy Sensors as a Tool to Detect Black Cumin Oil Adulteration

Black cumin oil adulteration has become a concern because it has numerous health benefits and a high price. Therefore, a simple, non-destructive, and rapid method to identify adulterations in black seed oil is necessary to protect the quality of the oils. This study aimed to perform a non-invasive method to authenticate black cumin oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to identify pure black cumin oil and partial least squares regression (PLSR) to predict the adulterant levels. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). SIMCA and PLSR models provided a very high performance in detecting adulterated samples in all portable units. These portable units showed great potential for rapid and non-destructive monitoring to identify adulterated black cumin oils.

Camellia oil adulteration detection using fatty acid ratios and tocopherol compositions with chemometrics

The fatty acid ratios and tocopherol compositions, coupled to chemometrics were used to verify camellia oil (CAO) adulteration with corn oil (COO), rapeseed oil (RAO), rice bran oil (RBO), sesame oil (SEO), and soybean oil (SOO). Using hierarchical clustering analysis (HCA), all CAO samples were classified into one category accounting for their high oleic acid/α-linolenic acid, oleic acid/palmitic acid, oleic acid/linoleic acid ratios, and α-tocopherol%. Based on partial least squares-discriminant analysis (PLS-DA), well clustering could be observed among CAO, CAO + COO, CAO + SOO, CAO + RBO, CAO + SEO, as well as CAO + RAO samples, with higher than 97.67% of the total discrimination accuracy, at an adulteration ratio above 40%. Using soft-independent modelling of class analogy (SIMCA), the adulterated CAO samples (5%–100%) possessed good correct classification rates above 90.00%. Both two complementary classification methods were confirmed to be feasible and accurate for camellia oil authentication.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm−1. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Benchtop NIR data standardization on handheld spectrometers to identify paracetamol tablets

AbstractNear‐infrared spectroscopy (NIRS) allows innovative applications in terms of quality control, for example, in raw materials verification, in process analytical technology (PAT), and in discrimination between genuine and falsified medicines. The development of small and cheap handheld devices is expanding in the field, while trying to keep similar performances as benchtop instruments have. Considering traceability and quality control of drug compounds, this work is intended to identify 13 different paracetamol tablets on the Belgian market by using NIRS. The performances of a Fourier‐transform near‐infrared spectroscopy (FT‐NIR) benchtop and two handheld NIR spectrometers were investigated comparatively. All spectra were collected through the blister in the reflectance diffuse mode. NIR spectral fingerprints were pretreated and analyzed using principal component analysis (PCA) and classified with soft independent modeling of class analogy (SIMCA). The performances of the spectrometers were evaluated after standardization of the reference benchtop database to the handheld spectrometers. The instrumental response of the benchtop spectrometer was standardized towards the handheld device using the piecewise direct standardization (PDS) algorithm. These investigations permitted the advantages and limitations of NIR data standardization on predictive models to be pointed out. The SIMCA models based on the spectral data of the benchtop (B), handheld 1 (H1), and handheld 2 (H2) instruments had an accuracy of 99.2%, 97.7%, and 96.2%, respectively. The same accuracy was obtained with the models of the transferred benchtop spectra for the H1 and H2 devices. However, adding handheld spectra to the transferred benchtop spectra resulted in models with improved accuracy: from 97.7% to 98.5% for the H1 instrument and from 96.2% to 97.7% for the H2 instrument. The proposed strategy highlights the potential of using a reference database collected by NIRS for pharmaceutical analyses by handheld NIR spectrometers in terms of traceability.

Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.

Spectral pattern study of citrus black rot caused by Alternaria alternata and selecting optimal wavelengths for decay detection.

Fungal decay is one of the most common diseases that affect postharvest operations and sales of citrus. Sometimes, fungal disease develops and spreads inside the fruit and in the advanced stages of the disease, it appears apparent, so the use of efficient and reliable methods for early detection of the disease is very important. In this study, early detection of citrus black rot disease caused by Alternaria genus fungus was examined using spectroscopy. Jaffa oranges were inoculated with Alternaria alternata. The samples were inspected by spectroscopy (200–1100 nm) in the 1st, 2nd, and 3rd weeks after inoculation. The classification of healthy and infected samples and selection of most important wavelengths were conducted by soft independent modeling of class analogy (SIMCA). The most important wavelengths in the detection of healthy and infected samples of the 1st week were 507, 933, 937, and 950 nm with a classification accuracy of 60%. The most important wavelengths of the 2nd week were 522 and 787 nm with a classification accuracy of 60%. Also, wavelengths of 546, 660, 691, and 839 were found to be effective in the 3rd week with a classification accuracy of 100%.

Detection of nutshells in cumin powder using NIR hyperspectral imaging and chemometrics tools

Cumin is a valuable spice for medical and food applications. Nevertheless, reports of the presence of undeclared adulterants in cumin require the development of analytical methods for its authentication. The goal of this study was to develop a new analytical method based on NIR-HSI (900–1710 nm) and chemometrics to detect low-cost adulterants peanut shell, pecan shell and walnut shell in cumin. PCA was applied to investigate the spectral features of pure samples and mixtures. Soft Independent Modelling Class Analogy (SIMCA) was applied to classify pure cumin and adulterated samples, achieving an accuracy of 95 % for test samples. Partial Least Square Regression (PLSR) model based on selected variables using iPLS or GA showed a similar (for walnut shell) or better (peanut and pecan shell) performance than PLSR models based on a full wavelength with detection limits above 1% and RPD (Residual Prediction Deviation) values higher than 5, indicating excellent predictive ability. Chemical maps allow visualization of shell concentration and distribution in cumin samples. This work demonstrated the potential of NIR-HSI and chemometrics to detect and quantify nutshells in cumin powder.

Palmer Amaranth (Amaranthus palmeri S. Watson) and Soybean (Glycine max L.) Classification in Greenhouse Using Hyperspectral Imaging and Chemometrics Methods

HighlightsHyperspectral image processing was used to classify Palmer amaranth and soybean species.Chemometrics methods (PCA, PLS-DA, and SIMCA) were used to extract features and establish classification models.Abstract. Herbicide-resistant weed species are one of the largest threats to modern agriculture, as ineffective weed control results in significant yield losses or increased costs through alternatives such as mechanical methods. Palmer amaranth (Amaranthus palmeri S. Watson) has been one of the most troublesome weeds. Its identification through the adoption of site-specific weed management systems will help farmers select more appropriate control options to reduce costs and improve efficacy, resulting in increased farm revenue. In this study, a pixel-wised method was evaluated for the classification of Palmer amaranth and soybean (Glycine max L.). A pushbroom hyperspectral imagery acquisition system was used to collect imagery from 224 spectral bands ranging from 400 to 1000 nm. Greenhouse experiments were conducted in three different runs. Greenhouse-grown plants were evaluated to generate predictive models from paired samples generated with 56 replications in each run. Data collection occurred weekly when Palmer amaranth plants were between approximately 2.5 and 12.7 cm tall. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were tested to classify Palmer amaranth and soybean. Half of the dataset (Palmer amaranth = 42, soybean = 42) was used to train the models, and the other half (Palmer amaranth = 42, soybean = 42) was used to test model performance. Preliminary results showed that the PLS-DA model with PLS factors as input had a cumulative variation (R2Y(cum)) of 60% and predictive ability (Q2Y(cum)) of 60%. The SIMCA model showed a cumulative variation of 85% and a predictive ability of 82%. Overall, this study illustrated the capability of hyperspectral imagery to classify Palmer amaranth and soybean, which will increase the efficiency of weed control in modern agriculture. Keywords: Chemometrics methods, Hyperspectral imaging, Palmer amaranth classification.

Infrared spectroscopy combined with chemometrics as a convenient method to detect adulterations in cooking/stretching process in commercial cheese

Stretching and kneading of the curd during fresh Kashar cheese manufacturing must take place in hot water; dry cooking/stretching of the curd with the assistance of emulsifying salts is not allowed. However, some producers in recent years tend to label their processed cheese as “Kashar cheese” resulting in unfair economic gain and consumer deception. Near-infrared (NIR) diffuse reflectance and mid-infrared (MIR) attenuated total reflectance (ATR) spectroscopy were assessed for the fast and convenient identification of these two types of cheese. Soft independent modeling of class analogy (SIMCA) models of both NIR and MIR-ATR spectra were developed; the latter gave superior separation due to the greater inter-class distance originating from better-resolved peaks associated with phosphates (6.7 for MIR-ATR versus 3.2 for NIR). Information Theory determined two and three variables were enough for MIR and NIR spectral data classification, respectively; quadratic discriminant and support vector machine provided 100% accuracy for class prediction.