Multivariate prototype approach for authentication of food products

Analytical capabilities of chemiresistive microsensor arrays in a simulated Martian atmosphere

We aimed to identify the browning of white adipocytes using partial least squares regression (PLSR), infrared spectral biomarkers, and partial least squares discriminant analysis (PLS-DA) with FTIR spectroscopy instead of molecular biology. PLSR helps distinguish human beige adipocytes treated with norepinephrine and rosiglitazone. When PLSR was based on the selected regions of 3997–3656 and 1618–938 cm−1, PLSR achieved an R2 of cross-validation of 88.95, a root mean square error of cross validation (RMSECV) of 2.13, and a ratio performance deviation (RPD) of 3.01. Infrared spectral biomarkers [1635 cm−1 (β-sheet amide I), 879–882, 860–3 cm−1 (A-form helix), and 629–38 cm−1 (OH out-of-plane bending)] were identified in human beige adipocytes based on spectral differences between human beige adipocytes and human white adipocytes, principal component analysis-linear discriminant analysis (PCA-LDA) cluster vector, U-test, and Fisher’s score per wavenumber. PLS-DA yielded a useful classification of adipocytes and expression distribution of adipogenesis genes in adipocytes. PLSR, infrared spectral biomarkers, and PLS-DA using FTIR spectroscopy are proposed as effective tools for identifying specific biological activities in a limited environment through features that do not require labeling and are relatively inexpensive in terms of time and labor.

Comparison of the performance of multiclass classifiers in chemical data: Addressing the problem of overfitting with the permutation test

Geographical discrimination of red garlic (Allium sativum L.) using fast and non-invasive Attenuated Total Reflectance-Fourier Transformed Infrared (ATR-FTIR) spectroscopy combined with chemometrics

Advances in the individual authentication of cocoa beans: Vis/NIR spectroscopy as a tool to distinguish fermented from unfermented beans and classify genotypes in the eastern Amazonia

Breast cancer is the most incident cancer worldwide. The evaluation of molecular subtypes and their biomarkers plays an essential role in prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal growth factor Receptor-type 2 (HER2), and Ki67. Based on these, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). The gold standard for this analysis is histology and immunohistochemistry, semi-quantitative techniques that present inter-laboratory and inter-observer variations. The Fourier Transform Infrared micro-spectroscopy (micro-FTIR), which provides hyperspectral images with biochemical information of biological tissues, is applied together with artificial intelligence (AI) for cancer evaluation. In this thesis, twenty samples of two breast cancer cell lines, BT-474 and SK-BR-3, were used to define the optimal number of co-added scans for machine learning (ML) techniques. Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) models were used. Sixty hyperspectral images of 320x320 pixels were collected from thirty patients of a human breast biopsies microarray, each containing a breast cancer (CA) and an adjacent tissue (AT) core. Automated methods based on K-Means clustering were developed for data organization and pre-processing to one-dimensional (1D) and two-dimensional (2D) data. The dataset was used to train two new deep learning models for breast cancer subtype evaluation: CaReNet-V1, a 1D Convolutional Neural Network (CNN); and CaReNet-V2, a 2D CNN. All ML models achieved similar performances with the b256_064 (256 background scans and 64 sample scans), b256_128, and b128_128 groups, where the best accuracy of 0.995 was presented by the XGB model. The b256_064 was established as the ideal among the three due to the shortest acquisition time. The K-Means-based method enabled fully automated preprocessing and organization, improving data quality and optimizing CNN training. CaReNet-V1 effectively classified CA and AT (individual spectra test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and 0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled the evaluation of the most contributing wavenumbers to the predictions, providing a direct relationship with the biochemical content of the samples. CaReNet-V2 demonstrated better performance than 1D, with test accuracies above 0.84, and enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). The Ki67 percentage regression demonstrated an absolute mean error of 3.6%. On the other hand, its impact evaluation by wavenumber was inferior to 1D. Thus, this study indicates image-based AI techniques using micro-FTIR as potential providers of additional information to pathological reports, also serving as patient biopsy screening techniques.

Authentication of PDO saffron of L'Aquila (Crocus sativus L.) by HPLC-DAD coupled with a discriminant multi-way approach

Canestrato di Castel del Monte (CCM) is a traditional sheep cheese from the Abruzzo region of Italy, strongly linked to local pastoral practices and characterized by high cultural and commercial value. Ensuring its authenticity is therefore essential to protect both producers and consumers. In this study, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modelling was investigated for the classification of traditional sheep cheeses. A dataset of approximately 2000 spectra obtained from Canestrato di Castel del Monte (CCM), low-ripening CCM, and Pecorino Toscano was analyzed using different modelling strategies. Partial Least Squares Discriminant Analysis (PLS-DA) and Sequential Preprocessing through Orthogonalization combined with Linear Discriminant Analysis (SPORT-LDA) were first applied to simultaneously separate the three categories. Subsequently, a class-modelling approach based on Soft Independent Modelling of Class Analogy (SIMCA) was used to authenticate CCM and low-ripening cheeses. The discriminant models achieved excellent classification performance: accuracies close to 100% for CCM and low-ripening CCM and around 95% for Pecorino Toscano. SIMCA provided reliable rejection of non-target samples, although with lower sensitivity compared to discriminant approaches. Overall, the results demonstrate that ATR-FTIR spectroscopy coupled with appropriate chemometric modelling represents a powerful strategy for the authentication and classification of traditional sheep cheeses.

Untargeted metabolomics is a well-established technique and a powerful tool to find potential plasma biomarkers for early diagnosing hereditary transthyretin amyloidosis. Hereditary transthyretin amyloidosis (ATTRv) is a disabling and fatal disease with different clinical features such as polyneuropathy, cardiomyopathy, different gastrointestinal symptoms and renal failure. Plasma specimens collected from 27 patients with ATTRv (ATTRV30M), 26 asymptomatic TTRV30M carriers and 26 control individuals were subjected to gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS)-based metabolomics analysis. Partial least squares discriminant and univariate analysis was used to analyse the data. The models constructed by Partial least squares-discriminant analysis (PLS-DA) could clearly discriminate ATTRV30M patients from controls and asymptomatic TTRV30M carriers. In total, 24 plasma metabolites (VIP > 1.0 and p < .05) were significantly altered in ATTRV30M patient group (6 increased and 18 decreased). Eleven of these distinguished the ATTRV30M group from both controls and TTRV30M carriers. Plasma metabolomics analysis revealed marked changes in several pathways in patients with ATTRV30M amyloidosis. Statistical analysis identified a panel of biomarkers that could effectively separate controls/TTRV30M carriers from ATTRV30M patients. These biomarkers can potentially be used to diagnose patients at an early stage of the disease.

In this work, an investigation using UHPLC-Q-Orbitrap-MS and multivariate statistics was conducted to obtain the lipid fingerprint of Camembert cheese and to explore its correlated variation with respect to X-ray irradiation treatment. A total of 479 lipids, categorized into 16 different lipid subclasses, were measured. Furthermore, the identification of oxidized lipids was carried out to better understand the possible phenomena of lipid oxidation related to this technological process. The results confirm that the lipidomic approach adopted is effective in implementing the knowledge of the effects of X-ray irradiation on food and evaluating its safety aspects. Furthermore, Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied showing high discriminating ability with excellent values of accuracy, specificity and sensitivity. Through the PLS-DA and LDA models, it was possible to select 40 and 24 lipids, respectively, including 3 ceramides (Cer), 1 hexosyl ceramide (HexCer), 1 lysophosphatidylcholine (LPC), 1 lysophosphatidylethanolamine (LPE), 3 phosphatidic acids (PA), 4 phosphatidylcholines (PC), 10 phosphatidylethanolamines (PE), 5 phosphatidylinositols (PI), 2 phosphatidylserines (PS), 3 diacylglycerols (DG) and 9 oxidized triacylglycerols (OxTG) as potential markers of treatment useful in food safety control plans.

This study investigated the effect of high pressure processing (HPP) on the fatty acids and amino acids content in New Zealand Diamond Shell (Spisula aequilatera), Storm Shell (Mactra murchisoni), and Tua Tua (Paphies donacina) clams. The clam samples were subjected to HPP with varying levels of pressure (100, 200, 300, 400, 500, and 600 MPa) and holding times (5 and 600 s) at 20 °C. Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were deployed to fingerprint the discriminating amino and fatty acids post-HPP processing while considering their inherent biological variation. Aspartic acid (ASP), isoleucine (ILE), leucine (LEU), lysine (LYS), methionine (MET), serine (SER), threonine (THR), and valine (VAL) were identified as discriminating amino acids, while C18:0, C22:1n9, C24:0, and C25:5n3 were identified as discriminating fatty acids. These amino and fatty acids were then subjected to mixed model ANOVA. Mixed model ANOVA was employed to investigate the influence of HPP pressure and holding times on amino acids and fatty acids in New Zealand clams. A significant effect of pressure levels was reported for all three clam species for both amino and fatty acids composition. Additionally, holding time was a significant factor that mainly influenced amino acid content. butnot fatty acids, suggesting that hydrostatic pressure hardly causes hydrolysis of triglycerides. This study demonstrates the applicability of OPLS-DA in identifying the key discriminating chemical components prior to traditional ANOVA analysis. Results from this research indicate that lower pressure and shorter holding time (100 MPa and 5 s) resulted in the least changes in amino and fatty acids content of clams.

The growth and development of schistosome has been affected in the immunodeficient hosts. But it remains unresolved about the molecular mechanisms involved in the development and reproduction regulation of schistosomes. This study tested and compared the metabolic profiles of the male and female Schistosoma japonicum worms collected from SCID mice and BALB/c mice at 5 weeks post infection using liquid chromatography tandem mass spectrometry (LC-MS/MS) platform, in which the worms from SCID mice were the investigated organisms and the worms from BALB/c mice were used as the controls. There were 1015 ion features in ESI+ mode and 342 ion features in ESI- mode were identified after filtration by false discovery rate. Distinct metabolic profiles were found to clearly differentiate both male and female worms in SCID mice from those in BALB/c mice using multivariate modeling methods including the Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). There were more differential metabolites in female worms than in male worms between SCID mice and BALB/c mice. And common and uniquely perturbed metabolites and pathways were identified among male and female worms from SCID mice when compared with BALB/c mice. The enriched metabolite sets of the differential metabolites in male worms between SCID mice and BALB/c mice included bile acid biosynthesis, taurine and hypotaurine metabolism, sphingolipid metabolism, retinol metabolism, purine metabolism, etc. And the enriched metabolite sets of differential metabolites in female worms included retinol metabolism, alpha linolenic acid and linoleic acid metabolism, purine metabolism, sphingolipid metabolism, glutamate metabolism, etc. Further detection and comparison in transcript abundance of genes of the perturbed retinol metabolism and its associated meiosis process in worms identified clues suggesting accumulated retinyl ester and perturbed meiotic process. These findings suggested an association between the schistosome with retarded growth and development in SCID mice and their perturbed metabolites and metabolic pathways, and provided a new insight into the growth and development regulation of S. japonicum worms from the metabolic level, which indicated great clues for discovery of drugs or vaccines against the parasites and disease with more researches.

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley &amp; Sons, Ltd.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Near infrared (NIR) spectroscopy with chemometrics was used for rapid source identification and component quantification of Arnebiae Radix. The modeling parameters were determined using the processing trajectory method. The quantitative model of target components was established using the model fusion method. The root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP), determination coefficient of calibration (Rcal 2 ) and validation (Rpre 2 ), and the ratio of standard error of prediction to standard deviation (RPD) were used to evaluate the constructed model. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin partial least square (PLS) model established using the step-by-step strategy were 1.3004 mg/g, 0.9453, 1.3833 mg/g, 0.9544, and 4.6650, respectively. The RMSEC, Rcal 2, RMSEP, Rpre 2 , and RPD of total pigment PLS model were 4.5095 mg/g, 0.9533, 4.5679 mg/g, 0.9534, and 3.0509, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of β-β'-dimethylacryloacanin Bagging-PLS model established using the process trajectory were 1.6126 mg/g, 0.9159, 1.2254 mg/g, 0.9649, and 5.2662, respectively. The RMSEC, Rcal 2 , RMSEP, Rpre 2 , and RPD of the total pigment Bagging-PLS model were 4.3499 mg/g, 0.9565, 4.4662 mg/g, 0.9116, and 3.1205, respectively. The accuracy of quantitative model may be improved using the process trajectory and model fusion method. Partial least squares discriminant analysis (PLS-DA) was used to establish the discriminant models of different sources of Arnebiae Radix. The predicted accuracy rate was 100%. The method is simple, rapid, and demonstrated for the discrimination and quantitative analysis of the main active ingredients of Arnebiae Radix.