Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Rapid identification of chemical compositions from three species of Siegesbeckiae Herba by ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-mass spectrometry in combination with deoxyribonucleic acid barcoding

Siegesbeckiae Herba, a traditional Chinese medicine, originates from Siegesbeckia orientalis, S. glabrescens, and S. pubescens in the Pharmacopoeia of the People's Republic of China. However, accurate identification of decoction pieces from the three plants remains a challenge. In this study, 26 batches of Siegesbeckiae Herba were identified by deoxyribonucleic acid barcoding, and their chemical compositions were determined using ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-mass spectrometry. The results showed that the internal transcribed spacer 2 and internal transcribed spacer 1-5.8 S- internal transcribed spacer 2 sequences could distinguish three species. In total, 48 compounds were identified including 12 marker compounds screened for three species using the partial least square discriminant analysis. Among these, two diterpenoids 16-O-malonylkirenol and 15-O-malonylkirenol, and a novel diterpenoid 15,16-di-O-malonylkirenol were isolated and identified. A convenient method for the identification of Siegesbeckiae Herba was established using kirenol and 16-O-acetlydarutoside as control standards by thin-layer chromatography. Unexpectedly, none of the batches of S. orientalis contained kirenol, which did not meet the quality standards of Siegesbeckiae Herba, suggesting that the rationality of kirenol as a quality marker for S. orientalis should be further investigated. The results of this study will contribute to the quality control of Siegesbeckiae Herba.

Antimicrobial Agent against Methicillin-Resistant Staphylococcus aureus Biofilm Monitored Using Raman Spectroscopy

The prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)-a leading cause of infections-forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with principal component analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of the incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.

Rapid discrimination of the identity of infant formula by triple-channel models

Infant formula is related to children's life and health. However, the existing identification methods for infant formula are time-consuming, costly and prone to environmental pollution. Therefore, a simple, efficient and less polluting identification method for infant formula is urgently needed. The aim of this study was to distinguish between goat and cow infant formula using HS-SPME-GC-MS and E-nose combined with triple-channel models. The results indicated that the main difference of them attributed to thirteen volatile compounds and three sensor variables. Based on this, the linear discriminant and partial least squares discriminant analyses were conducted, and a multilayer perceptron neural network model was constructed to identify the commercial samples. There was a high percentage of correct classifications (>90%) in samples. Together, our work demonstrated that the volatile compounds of infant formula combined with chemometric analysis were effective and rapid for detecting two infant formulas.

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R² in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

Effects of cold plasma, high hydrostatic pressure, ultrasound, and high-pressure carbon dioxide pretreatments on the quality characteristics of vacuum freeze-dried jujube slices

Pretreatment combined with vacuum freeze-drying is an effective technique to extend the storage period of jujube fruits and reduce energy consumption and cost; however, the effects of pretreatment on the quality characteristics of jujube during vacuum freeze-drying remain unknown. In this study, the effects of cold plasma (CP), high hydrostatic pressure (HHP), ultrasound (US), high-pressure carbon dioxide (HPCD), and conventional blanching (BC) as pretreatments on the performance of vacuum freeze-dried jujube slices were investigated. The results indicated that the application of different pretreatments decreased the water activity and increased the rehydration capacity, owing to the pretreatment etching larger and more porous holes in the microstructure. Freeze-dried jujube slices pretreated with HPCD retained most of their quality characteristics (color, hardness, and volatile compounds), followed by the HHP- and US-pretreated samples, whereas samples pretreated with BC showed the greatest deterioration in quality characteristics, and hence, BC is not recommended as a pretreatment for freeze-dried jujube slices. Sensory evaluation based on hedonic analysis showed that jujube slices pretreated with HPCD and US were close to the control sample and scored highest. Compared to other pretreated samples and the control, freeze-dried jujube slices pretreated with HPCD showed the least degradation (4.93%) of cyclic adenosine monophosphate (cAMP), the highest contents of total phenol, total flavonoid, and l-ascorbic acid, and the highest antioxidant capacity. Partial least squares-discriminant analysis (PLS-DA) was performed to screen all the quality characteristic data of different pretreated samples, and 12 volatile compounds, including ethyl hexanoate and (E)-2-hexenal, along with color, l-ascorbic acid content, and cAMP content were found suitable to be used as discriminators for pretreated freeze-dried jujube slices. Therefore, non-thermal pretreatments, including HPCD, US, and HHP pretreatments, are promising techniques for the vacuum freeze-drying of jujube products.

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

PLS-DA and Vis-NIR spectroscopy based discrimination of abdominal tissues of female rabbits

Using Vis-NIR spectroscopy to distinguish gestational sac from other abdominal tissues is the key to diagnosing female rabbits' pregnancy by optical means. This study aims to demonstrate the gestational sac and other abdominal tissues (hair, skin, breast, muscle, cecum, small intestine) of rabbits can be identified using Vis-NIR spectroscopy in vitro. These tissues' raw NIR spectra were recorded in the Vis-NIR range (490-940 nm) with interactive mode. The raw spectra of tissues were analyzed by the principal component analysis (PCA), and were pre-processed using five spectral pre-processing techniques (moving average filter (MF), De-trending (DT), first-order derivative (D1), Multivariate scattering correction (MSC), and standard normal variate (SNV)) to reduce signal noises. The raw and pre-processed spectra were classified using partial least squares discrimination analysis (PLS-DA). Two-way and multi-way PLS-DA model was conducted to understand the classification of each tissue from the gestational sac and to understand the classification of all tissues from the gestational sac, respectively. SNV-PLS-DA model had the best performance, and its multi-way accuracy (Ac), determination coefficients (R²), and Q² were 0.89, 0.91, 0.77, respectively. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to select characteristic wavelengths (CWs). The SNV-SPA-PLS-DA model with eighteen CWs was better than the SNV-CARS-PLS-DA model. The results showed that Vis-NIR spectroscopy technology combined with PLS-DA could discriminate the gestational sac from the abdominal tissues. This study may help develop an optical diagnosis system for pregnant rabbits.

Effect of temperature abuse on quality and metabolites of frozen/thawed beef loins

The objective of this study was to examine the effect of temperature abuse prior to cold storage on changes in quality and metabolites of frozen/thawed beef loin. The aerobic packaged samples were assigned to three groups: refrigeration (4°C) (CR); freezing (–18°C for 6 d) and thawing (20±1°C for 1 d), followed by refrigeration (4°C) (FT); temperature abuse (20°C for 6 h) prior to freezing (–18°C for 6 d) and thawing (20±1°C for 1 d), followed by refrigeration (4°C) (AFT). FT and AFT resulted in higher volatile basic nitrogen (VBN) values than CR (p<0.05), and these values rapidly increased in the final 15 d. Cooking loss decreased significantly with an increase in the storage period (p<0.05). In addition, cooking loss was lower in the FT and AFT groups than in the CR owing to water loss after storage (p<0.05). A scanning electron microscope (SEM) revealed that frozen/thawed beef samples were influenced by temperature abuse in the structure of the fiber at 15 d. Metabolomic analysis showed differences among CR, FT, and AFT from partial least square discriminant analysis (PLS-DA) based on proton nuclear magnetic resonance (¹H NMR) profiling. The treatments differed slightly, with higher FT than AFT values in several metabolites (phenylalanine, isoleucine, valine, betaine, and tyrosine). Overall, temperature abuse prior to freezing and during thawing of beef loin resulted in accelerated quality changes.

Early pregnancy diagnosis of rabbits: A non-invasive approach using Vis-NIR spatially resolved spectroscopy

Pregnancy diagnosis is essential for rabbit's reproductive management. The early identification of non-pregnant rabbits allows for earlier re-insemination, increases the service rate, and reduces the laboring interval in commercial operations. The objective of this study was to establish the feasibility of using a Vis-NIR spatially resolved spectroscopy for diagnosing pregnancy in female rabbits. A total of 141 female rabbits, including 67 pregnant female rabbits (PRs) and 74 non-pregnant female rabbits (NPRs), were measured spectrally between 350 and 1000 nm with different source-detector distances (SDD). Different preprocessing methods were used to transform and enhance the spectral signal. A partial least squares-discriminant analysis (PLS-DA) classification model of the original and preprocessed spectra was established. The highest accuracy of the calibration set and prediction set was 91.75% and 86.05%, respectively. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were used to select characteristic wavelengths from the variables of VIP > 1 (Variable importance in projection),and four classification models were established based on selected wavelengths, including PLS-DA, support vector machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes. SPA-SVM was the optimal classification model, the sensitivity, specificity, and accuracy of the validation set and prediction set were 93.18%, 94.44%, 93.88%, 86.96%, 90.00%, 90.69% respectively. The results showed that Vis-NIR spatially resolved spectroscopy combined with classification models could discriminate the PRs and NPRs.

Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models

ABSTRACT

This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models.

HIGHLIGHTS

Rapid detection of Chinese-specific peony seed oil by using confocal Raman spectroscopy and chemometrics

Peony seed oil (PSO) is a new woody nut oil which is unique to China. Its unsaturated fatty acids are over 90% and are rich in α - linolenic acid. Although the PSO industry is in its infancy, it is bound to become a top vegetable oil food material because of its own advantages. The potential high commercial profit of its adulteration with cheap vegetable oil will be an important factor hindering the healthy development of PSO industry. It is of great significance to study the adulteration of PSO for preventing large-scale adulteration. In this study, the qualitative and quantitative analysis of PSO was realised based on Raman spectroscopy combined with chemometrics analysis, and the fatty acid composition of PSO was analysed according to Raman characteristic peaks. The technology can be applied to routine analysis and quality control of PSO.

Aniba rosaeodora (Var. amazonica Ducke) Essential Oil: Chemical Composition, Antibacterial, Antioxidant and Antitrypanosomal Activity

Aniba rosaeodora is one of the most widely used plants in the perfumery industry, being used as medicinal plant in the Brazilian Amazon. This work aimed to evaluate the chemical composition of A. rosaeodora essential oil and its biological activities. A. rosaeodora essential oil presented linalool (93.60%) as its major compound. The A. rosaeodora essential oil and linalool showed activity against all the bacteria strains tested, standard strains and marine environment bacteria, with the lower minimum inhibitory concentration being observed for S. aureus. An efficient antioxidant activity of A. rosaeodora essential oil and linalool (EC₅₀: 15.46 and 6.78 µg/mL, respectively) was evidenced by the inhibition of the 2,2-azinobis- (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical. The antitrypanosomal activity of A. rosaeodora essential oil and linalool was observed at high concentrations against epimatigote forms (inhibitory concentration for 50% of parasites (IC₅₀): 150.5 ± 1.08 and 198.6 ± 1.12 µg/mL, respectively), and even higher against intracellular amastigotes of T. cruzi (IC₅₀: 911.6 ± 1.15 and 249.6 ± 1.18 µg/mL, respectively). Both A. rosaeodora essential oil and linalool did not exhibit a cytotoxic effect in BALB/c peritoneal macrophages, and both reduced nitrite levels in unstimulated cells revealing a potential effect in NO production. These data revealed the pharmacological potential of A. rosaeodora essential oil and linalool, encouraging further studies.

Potential of 2D qNMR spectroscopy for distinguishing chicken breeds based on the metabolic differences

Two-dimensional quantitative NMR spectroscopy (2D qNMR) was set up and multivariate analyses were performed on metabolites obtained from breast meat extracts of broilers and four native chicken (KNC) strains. It can accurately identify more metabolites than 1D ¹H NMR via separation of peak overlap by dimensional expansion with good linearity, but has a problem of numerical quantification; Complementation of 1D and 2D qNMR is necessary. Among breeds, KNC-D had higher amounts of free amino acids, sugars, and bioactive compounds than others. Noticeable differences between KNCs and broilers were observed; KNCs contained higher amounts of inosine 5'-monophosphate, α-glucose, anserine, and lactic acid, and lower amounts of free amino acids and their derivatives. The 2D qNMR combined with multivariate analyses distinguished the breast meat of KNCs from broilers but showed similarities among KNCs. Also, 2D qNMR may provide fast metabolomics information compared to conventional analysis.

Discrimination of narcotic drugs in human urine based on nanoplasmonics combined with chemometric method

The detection of psychoactive substances is an important branch of modern analytical chemistry and has many legally and socially relevant implications. The use of a surface plasmon resonance (SPR)-based gene-nanoparticle system has emerged as a promising technique for the rapid and ultrasensitive detection of molecular species such as drugs of abuse in biofluids. However, the development of a viable screening tool for the detection of multiple classes of drugs in complex media is a considerable challenge because the existing techniques lack affinity toward certain species due to matrix interference. Our aim was to develop a simple optical sensor array for the classification of nine narcotic drugs in aqueous solution and human urine. The UV-vis spectra of DNA-gold nanoparticles in the presence of nine narcotic drugs (pentobarbital sodium, caffeine, morphine, remifentanil, fentanyl, ketamine, etomidate, carfentanil, and sulfentanyl) were distinctly different. Furthermore, the narcotic drugs present in aqueous solution and in human urine were classified correctly through partial least squares discriminant analysis (PLS-DA). Combination with a multi-sensor unit further improved the prediction accuracy of the PLS-DA models. The proposed method has potential for on-site drug detection and drug abuse screening.

Aniba canelilla (Kunth) Mez (Lauraceae): A Review of Ethnobotany, Phytochemical, Antioxidant, Anti-Inflammatory, Cardiovascular, and Neurological Properties

Aniba canelilla (Kunth) Mez, popularly known as “casca preciosa” (precious bark), falsa canela (cinnamon-scented) Casca-do-maranhão (bark of maranhão), and Folha-preciosa (precious leaf), is an aromatic species of the Lauraceae family, widely distributed in the Amazon region. In traditional medicine, it is indicated for the treatment of a great diversity of diseases, including digestive, respiratory, inflam]matory, painful, and central nervous system disorders, it is administered mainly in the form of tea or decoction orally. Its essential oil is referred to as a natural antioxidant for food preservation and disease control, showing great potential for use in the cosmetics, perfumery, and pharmaceutical products sector. The present review aimed to discuss critically and comprehensively the ethnobotanical characteristics, phytochemical constitution, and scientifically tested biological properties of A. canelilla, systematizing the knowledge about the species and proposing new perspectives for research and development. The chemical composition of A. canelilla includes 1-nitro-2-phenylethane, metyleugenol, eugenol, safrol, anabasin, anbin, tannin, α-pinene, b-pinene, b-felandren, b-caryophyllene, b-sesquifelandren, p-cymene, linalool, α-copaene, and spatulenol. Researches with ethanolic extracts, essential oils, and major constituents (1-nitro-2-phenylethane and metyleugenol) have revealed antioxidant, antinociceptive, anti-inflammatory, cardio-modulating, hypotensive (vasorelaxant), hypnotic, anxiolytic, anticholinesterase, and antibiotic properties (trypanomicidal, leishmanicidal, and antifungal). Some of these effects are potentially beneficial for aging-related diseases treatment, such as cardio and cerebrovascular, chronic inflammatory, neurological, and degenerative diseases. However, it is necessary to advance in the research of its clinical use and development of therapeutic products.

Analysis of the Volatile Components in Selaginella doederleinii by Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry

Selaginella doederleinii (SD) is a perennial medicinal herb widely distributed in China. In this study, the volatile components of SD from two regions (24 batches), namely Zhejiang and Guizhou, were determined by combining headspace solid phase microextraction and gas chromatography-mass spectrometry (HS-SPME/GC-MS). After investigating different influence factors, the optimal conditions for extraction were as follows: The sample amount of 1 g, the polydimethylsiloxane-divinylbenzene (PDMS-DVB) fiber of 65 µm, the extraction time of 20 min, and the extraction temperature of 100 °C. Based on the above optimum conditions, 58 volatiles compounds, including 20 terpenes, 11 alkanes, 3 alcohols, 6 ketones, 3 esters, 11 aldehydes, 1 ether, 1 aromatic, 1 phenol, and 1 furan, were found and identified in SD. Furthermore, hierarchical cluster analysis and principal component analysis were successfully applied to distinguish the chemical constituents of SD from two regions. Additionally, anethol, zingerone, 2,4-di-tert-butylphenol, ledene, hexyl hexanoate, α-cadinol, phytone, hinesol, decanal, octadecene, cedren, 7-tetradecene, copaene, β-humulene, 2-butyl-2-octenal, tetradecane, cedrol, calacorene, 6-dodecanone, β-caryophyllene, 4-oxoisophorone, γ-nonanolactone, 2-pentylfuran, 1,2-epoxyhexadecane, carvacrol, n-pentadecane, diisobutyl phthalate, farnesene, n-heptadecane, linalool, 1-octen-3-ol, phytane, and β-asarone were selected as the potential markers for discriminating SD from 24 habitats in Zhejiang and Guizhou by partial least squares discrimination analysis (PLS-DA). This study revealed the differences in the components of SD from different regions, which could provide a reference for the future quality evaluation.

Peripheral biomarkers allow differential diagnosis between schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders that pose important challenges for diagnosis by sharing common symptoms, such as delusions and hallucinations. The underlying pathophysiology of both disorders remains largely unknown, and the identification of biomarkers with potential to support diagnosis is highly desirable. In a previous study, we successfully discriminated SCZ and BD patients from healthy control (HC) individuals by employing proton magnetic resonance spectroscopy (¹H-NMR). In this study, ¹H-NMR data treated by chemometrics, principal component analysis (PCA) and supervised partial least-squares discriminant analysis (PLS-DA), provided the identification of metabolites present only in BD (as for instance the 2,3-diphospho-D-glyceric acid, N-acetyl aspartyl-glutamic acid, monoethyl malonate) or only in SCZ (as isovaleryl carnitine, pantothenate, mannitol, glycine, GABA). This may represent a set of potential biomarkers to support the diagnosis of these mental disorders, enabling the discrimination between SCZ and BD, and among these psychiatric patients and HC (as 6-hydroxydopamine was present in BD and SCZ but not in HC). The presence or absence of these metabolites in blood allowed the categorization of 182 independent subjects into one of these three groups. In addition, the presented data suggest disturbances in metabolic pathways in SCZ and BD, which may provide new and important information to support the elucidation and/or new insights into the neurobiology underlying these mental disorders.

Authentication of black cohosh (Actaea racemosa) dietary supplements based on chemometric evaluation of hydroxycinnamic acid esters and hydroxycinnamic acid amides

Ester and amide derivatives of hydroxycinnamic acids are found in black cohosh (Actaea racemosa) and other Actaea plants. These two compound groups were evaluated for authentication of black cohosh dietary supplements. The hydroxycinnamic acid esters (HCAE) were profiled by ultra-performance liquid chromatography-photodiode array detection (UPLC-PDA). The hydroxycinnamic acid amides (HCAA) were acquired simultaneously by mass spectrometry-multiple reaction monitoring (UPLC-MRM) mode. In contrast with the traditional HCAE method using 8 compounds, profiles of HCAA using only 4 feruloyl dopamine-O-hexosides was more convenient for peak by peak comparison. Partial least square discriminant analysis (PLS-DA) was applied to both HCAE and HCAA datasets. Authenticated plant samples of five Actaea species were randomly divided into training and test sets to build and validate the two PLS-DA models. Both models provided reasonable estimates for the classification of A. racemosa and other Actaea plant samples. However, HCAA model performs better in sensitivity, specificity, and accuracy. Assessment of supplement samples provided quite different results for the solid and liquid dietary supplement samples, indicating the dosage form could affect the composition of marker compounds. Graphical abstract.

Assessing utility of handheld laser induced breakdown spectroscopy as a means of Dalbergia speciation

Many species of Dalbergia are prized hardwoods, generally referred to as 'Rosewood,' and used in high-end products due to their distinctive hue and scent. Despite more than 58 species of Dalbergia being listed as endangered in Appendix 1 of The Convention on International Trade in Endangered Species of Fauna and Flora (CITES), the illegal logging and trade of this timber is ongoing. In this work, a handheld laser induced breakdown spectrometer (LIBS) was used to analyze seven Dalbergia species and two other exotic hardwood species to evaluate the ability of handheld LIBS for rapid classification of Dalbergia in the field. The KNN model of the classification presented 80% to 90% sensitivity for discriminating between Dalbergia species in the training set. PLS-DA models were based on a binary decision tree structure. Cumulatively, the PLS-DA decision tree model showed greater than 97% sensitivity and 99% selectivity for prediction of Dalbergia species included in the training set. The data presented in the following study are promising for the use of handheld LIBS devices and both KNN and PLS-DA models for applications in customs screenings at the port of entry of hard woods, among others.

Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics

The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.

Predictive modelling of colossal ATR-FTIR spectral data using PLS-DA: empirical differences between PLS1-DA and PLS2-DA algorithms

In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.

Fast discrimination of bacteria using a filter paper-based SERS platform and PLS-DA with uncertainty estimation

Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper-based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria. Graphical abstract This paper describes the discrimination of bacteria at the genus and species levels, after minimal sample preparation, using paper-based SERS substrates and PLS-DA with uncertainty estimation.

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Live cell Raman micro-spectroscopy is emerging as a promising bioanalytical technique for label-free discrimination of a range of different cell types (e.g. cancer cells and fibroblasts) and behaviors (e.g. apoptosis). The aim of this study was to determine whether confocal Raman micro-spectroscopy shows sufficient sensitivity and specificity for identification of primary human bronchial epithelial cells (HBECs) to be used for live cell biological studies in vitro. We first compared cell preparation substrates and media, considering their influence on lung cell proliferation and Raman spectra, as well as methods for data acquisition, using different wavelengths (488 nm, 785 nm) and scan protocols (line, area). Evaluating these parameters using human lung cancer (A549) and fibroblast (MRC5) cell lines confirmed that line-scan data acquisition at 785 nm using complete cell media on a quartz substrate gave optimal performance. We then applied our protocol to acquisition of data from primary human bronchial epithelial cells (HBEC) derived from three independent sources, revealing an average sensitivity for different cell types of 96.3% and specificity of 95.2%. These results suggest that Raman micro-spectroscopy is suitable for delineating primary HBEC cell cultures, which in future could be used for identifying different lung cell types within co-cultures and studying the process of early carcinogenesis in lung cell culture.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Raman spectroscopy paired with the partial least squares (PLS) method is commonly used for quantitative or qualitative analysis of complex samples. However, spectral shift induced by different Raman spectroscopy, different environment or different measured time will decrease the accuracy of the PLS model. In this work, the processing algorithms that improve the accuracy by removing the noise, background and varying sources of other spectral interference were first reviewed. The error induced by the spectral shift was analyzed and the formulas of the error were derived. The formulas were then used to calculate the theoretical error in the example of discriminating human and nonhuman blood. A comparison of the actual errors obtained from the mathematical method and experiment with the theoretical value demonstrated the effectiveness of the equation. The compensation for nonhuman blood according to the average error demonstrated the improvement of the accuracy. Finally, the non-uniform sampling of the Raman shift by charge-coupled device (CCD) was considered in the error equation. An accurate error equation was obtained. This work could help improve the stability of PLS models in the case of the spectral shift of the spectrometer in Raman spectroscopy.

Identification of Salvia species using high-performance liquid chromatography combined with chemical pattern recognition analysis

Salvia miltiorrhiza, also known as Danshen, is a widely used traditional Chinese medicine for the treatment of cardiovascular diseases and hematological abnormalities. The root and rhizome of Salvia przewalskii and Salvia yunnanensis have been found as substitutes for Salvia miltiorrhiza in the market. In this study, the chemical information of 14 major compounds in Salvia miltiorrhiza and its substitutes were determined using a high-performance liquid chromatography method. Stepwise discriminant analysis was adopted to select the characteristic variables. Partial least squares discriminant and hierarchical cluster analysis were performed to classify Salvia miltiorrhiza and its substitutes. The results showed that all of the samples were correctly classified both in partial least squares discriminant analysis and hierarchical cluster analysis based on the four compounds (caffeic acid, rosmarinic acid, salvianolic acid B, and salvianolic acid A). This method can not only distinguish Salvia miltiorrhiza and its substitutes, but also classify Salvia przewalskii and Salvia yunnanensis. The method can be applied for the quality assessment of Salvia miltiorrhiza and identification of unknown samples.

Discrimination of human and nonhuman blood using Raman spectroscopy with self-reference algorithm

We report a self-reference algorithm to discriminate human and nonhuman blood by calculating the ratios of identification Raman peaks to reference Raman peaks and choosing appropriate threshold values. The influence of using different reference peaks and identification peaks was analyzed in detail. The Raman peak at 1003  cm-1 was proved to be a stable reference peak to avoid the influencing factors, such as the incident laser intensity and the amount of sample. The Raman peak at 1341  cm-1 was found to be an efficient identification peak, which indicates that the difference between human and nonhuman blood results from the C-H bend in tryptophan. The comparison between self-reference algorithm and partial least square method was made. It was found that the self-reference algorithm not only obtained the discrimination results with the same accuracy, but also provided information on the difference of chemical composition. In addition, the performance of self-reference algorithm whose true positive rate is 100% is significant for customs inspection to avoid genetic disclosure and forensic science.

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

A new way to discriminate polluted wood by vibrational spectroscopies

In this work, two sets of samples were considered: field samples collected from local waste wood and synthetic samples made by mixing clean wood (including oak, beech, poplar) with typical organic pollutants: creosote, polychlorinated byphenils (PCBs), pentachlorophenol (PCP), cypermethrin, dodecyl dimethyl ammonium chloride (DDAC). Vibrational spectroscopy techniques were tested to detect organic pollutants in wood items. Raman and infrared spectroscopies were showed as fast, non-destructive and non-invasive fingerprint techniques for detection of organic molecules. Associated with principal component analysis, we have shown the evidence of quick detection of and discrimination of polluted wood items by kinds and versus concentration.

Statistical strategies to reveal potential vibrational markers for in vivo analysis by confocal Raman spectroscopy

The analysis of biological systems by spectroscopic techniques involves the evaluation of hundreds to thousands of variables. Hence, different statistical approaches are used to elucidate regions that discriminate classes of samples and to propose new vibrational markers for explaining various phenomena like disease monitoring, mechanisms of action of drugs, food, and so on. However, the technical statistics are not always widely discussed in applied sciences. In this context, this work presents a detailed discussion including the various steps necessary for proper statistical analysis. It includes univariate parametric and nonparametric tests, as well as multivariate unsupervised and supervised approaches. The main objective of this study is to promote proper understanding of the application of various statistical tools in these spectroscopic methods used for the analysis of biological samples. The discussion of these methods is performed on a set of in vivo confocal Raman spectra of human skin analysis that aims to identify skin aging markers. In the Appendix, a complete routine of data analysis is executed in a free software that can be used by the scientific community involved in these studies.