We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Physalis alkekengi L.var. franchetii (Mast.) Makino (PAF) is an important edible and medicinal plant resource in China. Historically, phytochemical studies have primarily examined the calyx and fruit due to their long-standing use in traditional Chinese medicine for their ability to clear heat and detoxify. Metabolites and bioactivities of other parts such as the leaves, stems and roots, are rarely studied. The study involved conducting metabolic profiling of five plant parts of PAF using UPLC-Q-Orbitrap-HRMS analysis, in conjunction with two bioactivity assays. A total of 95 compounds were identified, including physalins, flavonoids, sucrose esters, phenylpropanoids, nitrogenous compounds and fatty acids. Notably, 14 aliphatic sucrose esters, which are potentially novel compounds, were initially identified. Furthermore, one new aliphatic sucrose ester was purified and its structure was elucidated by 1D and 2D NMR analysis. The hierarchical clustering analysis and principal component analysis showed the close clustering of the root and stem, suggesting similarities in their chemical composition, whereas the leaf, calyx and fruit clustered more distantly. Orthogonal partial least-squares discriminant analysis results showed that 41 compounds potentially serve as marker compounds for distinguishing among plant parts. Variations in activity were observed among the plant parts during the comparative evaluation with biological assays. The calyx, leaf and fruit extracts showed stronger antibacterial and anti-inflammatory activities than the stem and root extracts, and 19 potential biomarkers were identified by S-plot analysis for the observed activities, including chlorogenic acid, luteolin, cynaroside, physalin A, physalin F, physalin J, apigetrin, quercetin-3β-D-glucoside and five ASEs, which likely explain the observed potent bioactivity.

This study analyzed the phenolic compounds, organic acids, sugars, and antioxidant activity in different conventional apple cultivars (Malus domestica) from the Serbian market. Polyphenol profiles, sugars, and organic acid contents were analyzed by HPLC, and antioxidant activity was examined by DPPH and FRAP. Notable findings included variations in phenolic compound presence, with certain compounds detected only in specific cultivars. \'Red Jonaprince\' exhibited the highest arbutin (0.86 mg/kg FW) and quercetin-3-rhamnoside content (22.90 mg/kg FW), while \'Idared\' stood out for its gallic acid content (0.22 mg/kg FW) and \'Granny Smith\' for its catechin levels (21.19 mg/kg FW). Additionally, malic acid dominated among organic acids, with \'Granny Smith\' showing the highest content (6958.48 mg/kg FW). Fructose was the predominant sugar across all cultivars. Chemometric analysis revealed distinct groupings based on phenolic and organic acid profiles, with \'Granny Smith\' and \'Golden Delicious\' exhibiting unique characteristics. Artificial neural network modeling effectively predicted antioxidant activity based on the input parameters. Global sensitivity analysis highlighted the significant influence of certain phenolic compounds and organic acids on antioxidant activity.

Quisqualis fructus (QF) is a traditional Chinese medicine (TCM) that it has a long history in the therapeutic field of killing parasites, eliminating accumulation, and stopping diarrhea. However, the therapeutic material basis of QF is remaining ambiguous nowadays. The geographical origin differences of QF are also usually ignored in the process of medication. In this study, the alcohol-aqueous soluble constituents in QF from different origins were systematically characterized and accurately measured by ultra-high performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and high-performance liquid chromatography (HPLC) respectively. Chemometric analysis was performed for origin differentiation and screening of potential quality marker (Q-marker). Finally, A total of 106 constituents were tentatively characterized in positive and negative ion modes, including 29 fatty acids, 26 organic acids, 11 amino acids and derivatives, 10 glycosides, 9 alkaloids and derivatives, and 21 other constituents. QF from different origins were effectively distinguished and 16 constituents were selected as the potential Q-markers subsequently. Four representative components (trigonelline, adenosine, ellagic acid, and 3,3\'-di-O-methylellagic acid) in QF samples were simultaneously determined. HPLC fingerprint analysis indicated that the similarity between 16 batches of QF was in the range of 0.870-0.999. The above results provide some insights for the research on the pharmacodynamic constituents, quality control, and geographical discrimination of QF.

This study aims to establish a rapid and convenient microwave-assisted digestion method for sample pretreatment to determine amino acid profiles in natural products. This method was applied to analyze the amino acid profiles of Quisqualis Fructus (QF) from different planted origins. The microwave-assisted digestion conditions were optimized by a response surface methodology (RSM), and 17 amino acids in different planted origins of QF were determined by an automatic amino acid analyzer according to the optimized digestion conditions. The contents of 17 amino acids in QF from different planted origins were further analyzed by fingerprint and chemometric analysis. The temperature of microwave digestion at 167 °C, time of microwave digestion at 24 min, and a solid-liquid ratio of 46.5 g/mL was selected as the optimal digestion conditions. The total content of 17 amino acids in QF from different planted origins ranged from 71.88 to 91.03 mg/g. Amino acid composition and nutritional evaluation indicated that the content of medicinal amino acids was higher than aromatic amino acids. The results of fingerprint analysis reflected that the similarity between the 16 batches of QF ranged from 0.889 to 0.999, while chemometrics analysis indicated amino acid content in QF varied from different planted origins, and six important differential amino acids were screened. Compared with the traditional extraction method, microwave-assisted digestion with response surface optimized has the advantages of rapidity, convenience, and reliability, which could be used to study the amino acid profiles in natural products. The amino acid profile of QF indicated that it has a rich medicinal nutritional value. Different planted origins of QF have a high degree of similarity and could be effectively distinguished by chemometric analysis.

Consumer acceptability of beers is influenced by product formulation and processing conditions, which impart unique sensory profiles. This study used multivariate techniques to evaluate at-home consumer sensory acceptability of six commercial beers considering their style, fermentation type, and chemical composition. Samples included top-fermented beers (American India Pale Ale and Stout) and bottom-fermented beers (Pilsner, zero-alcohol Pilsner, Vienna Lager, and Munich Dunkel). Beer consumers (n = 50) conducted sensory hedonic, check-all-that-apply (CATA) and just-about-right (JAR) tests. Chemometric variables included iso-alpha-acids, hordenine, and volatile aromatic compounds, quantified by chromatographic methods, whereas bitterness units (IBU) were determined spectrophotometrically. Lager beers had higher acceptability than top-fermented beer (p < .05) for all attributes. Light-colored beers and medium-height foams had the highest liking scores for visual sensory attributes. Higher concentrations of bitter-tasting molecules, hordenine, and acidity decreased the liking scores of top-fermented (Ale) beers, as a sensory penalty analysis suggested. In contrast, the most favored beers (Pilsners and Munich Dunkel) contained higher fusel alcohol esters linked to fruity aromatic notes. Although a low conversion rate of fatty acids into fruity esters was noted in nonalcoholic Pilsner, its overall liking score was not statistically different from the alcoholic version. However, consumers perceived the nonalcoholic Pilsner as less bitter than its alcoholic counterpart even when IBUs were nonsignificantly different. This study emphasized the significance of understanding beer chemometrics to comprehend consumer acceptability, highlighting the crucial role of bitter molecules. Hence, hordenine, acidity, and volatile contents provided additional and valuable insights into consumer preferences.

BACKGROUND: Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound\'s correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.
OBJECTIVE: The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.
METHODS: The searching keywords \"biochemometric,\" \"chemometric,\" \"chromatography,\" \"natural products bioassay,\" and \"bioassay\" were selected to search the published articles between 2010-2023 using different search engines including \"Pubmed\", \"Web of Science,\" \"ScienceDirect,\" and \"Google scholar.\"
RESULTS: An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.
CONCLUSIONS: Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

With the proliferation of the consumer\'s awareness of wine provenance, wines with unique origin characteristics are increasingly in demand. This study aimed to investigate the influence of geographical origins and climatological characteristics on grapes and wines. A total of 94 anthocyanins and 78 non-anthocyanin phenolic compounds in grapes and wines from five Chinese viticultural vineyards (CJ, WH, QTX, WW, and XY) were identified by UHPLC-QqQ-MS/MS. Chemometric methods PCA and OPLS-DA were established to select candidate differential metabolites, including flavonols, stilbenes, hydroxycinnamic acids, peonidin derivatives, and malvidin derivatives. CCA showed that malvidin-3-O-glucoside had a positive correlation with mean temperature, and quercetin-3-O-glucoside had a negative correlation with precipitation. In addition, enrichment analysis elucidated that the metabolic diversity in different origins mainly occurred in flavonoid biosynthesis. This study would provide some new insights to understand the effect of geographical origins and climatological characteristics on phenolic compounds in grapes and wines.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.