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The primary challenge hindering the broad application of surface-enhanced Raman scattering (SERS) is the variability in substrate performance due to site differences, leading to unstable detection results. Thus, the current work reports the constant potential deposition of gold (Au) nanostructure on a hybrid nanoporous gold (npAu)-Au mirror template to generate highly stable monolithic Au-saturated npAu@Au-mirror substrate. By systematically adjusting electrochemical variables, different sizes, shapes, and nanogaps of Au nanostructure are generated with high-intensity electromagnetic field regions (hot junctions) for enhanced SERS response. The resulting reproducible and enhanced SERS response of the Au-saturated npAu@Au-mirror mainly originates from i) the precise control of potential and accumulation time (-0.3 V and t = 100 s) to generate uniform and symmetric particle size, dispersion, and nanogaps formation through the reduction, nucleation, and growth of Au nanoparticles; ii) free selective dealloying and complete immersion of Au 50 Ag 50 to ensure identical conditions across the alloy surface. The metrics for substrate efficiency were evaluated for reproducibility (n = 100, RSD = 14.22%), signal stability (storage time 25 days and variation in batches and multiple synthesis runs) and enhancements (enhancement factor∼1.3 × 10 7 ). The substrate was extended for the rapid and direct malachite green detection in fish samples with a detection limit of 1.7 × 10 -9  mol L -1 and good recoveries (96.69 ± 2.18%∼98.51 ± 0.63%). The monolithic Au-saturated npAu@Au-mirror substrate might be implemented for routine analysis of other target types in food safety-related applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their application. The emergence of machine learning (ML) has enhanced the efficiency of different sensors for food safety assessment. The ML combined with various noninvasive biosensors has been implemented efficiently to monitor food safety by considering the stability of bio-recognition molecules. This review comprehensively summarizes the application of ML-powered biosensors to investigate food safety. Initially, different detector-based biosensors using biological molecules with their advantages and disadvantages and biosensor-related various ML algorithms for food safety monitoring have been discussed. Next, the application of ML-powered biosensors to detect antibiotics, foodborne microorganisms, mycotoxins, pesticides, heavy metals, anions, and persistent organic pollutants has been highlighted for the last five years. The challenges and prospects have also been deliberated. This review provides a new prospect in developing various biosensors for multi-food contaminants powered by suitable ML algorithms to monitor in-situ food safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu 2 O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables., Results: The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (R C ), prediction (R P ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; R C  = 0.915; RMSECV = 1.39; R P  = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while R C  = 0.9723, RMSECV = 0.81, R P  = 0.9114, RMSEP = 1.45 and RPD = 2.59., Conclusion: The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry., (© 2023 Society of Chemical Industry.)

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R C and R P ), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R C  = 0.8793, RMSECV = 7.91 mg/100 g, R P  = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1 st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (R p ) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Monitoring chlorophyll during Tencha (the raw ingredient for matcha) processing is critical for determining the matcha's color and quality. The purpose of this study is to explore the mechanism of chlorophyll changes during Tencha processing and evaluate the viability of predicting its content by a portable near-infrared (NIR) spectrometer. The Tencha samples' spectral data were first preprocessed using various preprocessing techniques. Subsequently, iteratively variable subset optimization (IVSO), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS) were used to optimize and build partial least square (PLS) models. The CARS-PLS models achieved the best predictive accuracy, with correlation coefficients of prediction (R p ) = 0.9204 for chlorophyll a, R p  = 0.9282 for chlorophyll b, and R p  = 0.9385 for total chlorophyll. These findings suggest that NIR spectroscopy could be used as a surrogate for immediate quantification and monitoring of chlorophyll during Tencha processing., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

A highly structured fluorometric bioassay has been proposed for screening Staphylococcus aureus (S. aureus). The study exploits (i) the spectral attributes of the hexagonal NaYF 4 :Yb,Er upconversion nanoparticle (UCNP)-coated 3-aminopropyl)triethoxysilane; (ii) the intrinsic non-fluorescent quenching features of the highly stable dark blackberry (BBQ®-650) receptor; (iii) the aptamer (Apt-) biorecognition and binding affinity, and (iv) the complementary DNA hybridizer-linkage efficacy. The principle relied on the excited state energy transfer between the donor Apt-labeled NH 2 -UCNPs at the 3' end, and cDNA-grafted BBQ®-650 at the 5' end, as the effective receptors. The donor moieties in proximity (< 10.0 nm) trigger hybridization with the cDNA-grafted dark BBQ®-650, as the receptors of energy from the 2 F 5/2 level of Yb 3+ ions to initiate the Förster resonance energy transfer pathway. This was confirmed by the decline in the excited-state lifetimes from 223.52 μs (τ 1 ) to 179.26 μs (τ 2 ). The existence of the target S. aureus in the bioassay attracts the Apt- resulting in the detachment of the acceptor, and disintegration of the complex configuration via conformation reversal. The re-activated fluorescence monitored at λex/em = 980/652 nm, as a function of the logarithmic concentration of S. aureus (42 to 4.2 × 10 8  CFU mL -1 ), yielded an ultra-low detection response of 2.0 CFU mL -1 . The bioassay screening of S. aureus in real samples revealed satisfactory recoveries (92.44-107.82%) and validation results (p > 0.05). Hence, the comprehensive Apt-labeled NH 2 -UCNPs-cDNA-grafted dark BBQ®-650 bioassay offered fast and precise S. aureus screening in food and environmental settings., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIR LL , CSA-MIR LL , and NIR-MIR LL ) and mid-level (CSA-NIR ML , CSA-MIR ML , and NIR-MIR ML ) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R 2 p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R 2 p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R 2 p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIR ML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Food quality and nutrition have received much attention in recent decades, thanks to changes in consumer behavior and gradual increases in food consumption. The demand for high-quality food necessitates stringent quality assurance and process control measures. As a result, appropriate analytical tools are required to assess the quality of food and food products. VOCs analysis techniques may meet these needs because they are nondestructive, convenient to use, require little or no sample preparation, and are environmentally friendly. In this article, the main VOCs released from various foods during transportation, storage, and processing were reviewed. The principles of the most common VOCs analysis techniques, such as electronic nose, colorimetric sensor array, migration spectrum, infrared and laser spectroscopy, were discussed, as well as the most recent research in the field of food quality and safety evaluation. In particular, we described data processing algorithms and data analysis captured by these techniques in detail. Finally, the challenges and opportunities of these VOCs analysis techniques in food quality analysis were discussed, as well as future development trends and prospects of this field.

Extensively employed pesticide in agriculture causes residue in food products that would threaten public health safety. The surface-enhanced Raman scattering (SERS) signal reliant on double sensing of carbendazim and thiabendazole in a single step is achieved without the aid of any bio-recognition element. A label-free anisotropic bimetallic hollow Au/Ag nanostars (HAu/Ag NS) SERS substrate was synthesized with numerous hot spots for Raman molecule through a galvanic displacement-free deposition. The individual and mixed analyte calibration results were compared based on the identified peak at 1224 (carbendazim) and 778 (thiabendazole) cm -1 and exhibited insignificant differences. The sensor could detect carbendazim and thiabendazole up to 4.28 × 10 -4 and 6.04 × 10 -4 µg·g -1 or µg·mL -1 in both individual and mixture of their extract. The recovery for accuracy and precision analysis was 91.54-98.26 % in rice and water. Finally, validation results were achieved satisfactorily (p > 0.05) with HPLC., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Edible crude palm oil (CPO) is a vital oil utilized in various industries, including food, pharmaceuticals, and domestic cooking. Unfortunately, reports of CPO adulteration with harmful Sudan dyes have surfaced over the years. Surface-enhanced Raman spectroscopy (SERS) and chemometrics were employed to detect Sudan dyes adulteration in CPO within 900 - 1800 cm - 1 Raman peak. The concentration of Sudan dyes detected in CPO samples ranged between 0.005 and 4 ppm. The principal component analysis (PCA) model detected Sudan II and Sudan IV in CPO with 99.88 and 99.90% accuracy. Linear discriminant analysis (LDA) and K-Nearest Neighbors (KNN) also recorded high detection rates of Sudan II and IV dyes in CPO. Sudan II and IV dyes could be detected at 0.0028 ppm and 0.0019 ppm by this sensor. The performance of the Au@Ag SERS sensor was comparable to that of HPLC. This study proved SERS and chemometrics can be used to authenticate edible CPO., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction ( R p ), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA ( R p = 0.937, RPD = 3.38) and CP ( R p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001-1000 µg/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 µg/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra- and interday recovery for both samples were in the range of 92.62-96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

A smartphone-based colorimetric sensor array system was established for discrimination of rice varieties having different geographical origins. Purposely, aroma profiling of nine rice varieties was performed using solid-phase microextraction gas chromatography-mass spectrometry. Alcohols, aldehydes, alkanes, ketones, heterocyclic compounds, and organic acids represent the abundant compounds. Colorimetric sensor array system produced a characteristic color difference map upon its exposure to volatile compounds of rice. Discrimination of rice varieties was subsequently achieved using principal component analysis, hierarchical clustering analysis, and k-nearest neighbors. Rice varieties from same geographical source were clustered together in the scatter plot of principal component analysis and hierarchical clustering analysis dendrogram. The k-nearest neighbors algorithm delivered optimal results with discrimination rate of 100% for both calibration and prediction sets using sensor array system. The smartphone-based colorimetric sensor array system and gas chromatography technique were able to effectively differentiate rice varieties with the advantage of being simple, rapid, and low-cost., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The emerging fruit wastes valorization tactic is a strategy for minimizing the dependence on toxic solvents and chemicals commonly used in the preparation of nanoparticles (NPs). Furthermore, the NPs have exhibited promising antimicrobial applications against foodborne pathogens. Hence, a timely review of this topic is in demand to provide a clear insight into the subject. In this article, the synthesis of silver and gold NPs from fruit wastes and their antimicrobial application against foodborne pathogens are reviewed. The extraction method, mechanism of NPs formation and influences of various experimental parameters on the shape and size of the NPs are described. In the second part of the article, antimicrobial activities against foodborne pathogens regarding the nature, optimum composition, surface structure, synergism and morphology of the NPs are reviewed. Furthermore, challenges and future trends related to the synthesis and antimicrobial application of fruit wastes-mediated NPs are discussed., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R P  = 0.9616, RPD = 3.807 for TPC, R P  = 0.9657, RPD = 3.367 for TFC, R P  = 0.9683, RPD = 2.728 for ABTS assay, and R P  = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Considering food safety and limitations of biorecognition elements, this study focused on the development of a novel method for predicting mercury (Hg 2+ ) in fish and water samples using surface-enhanced Raman scattering (SERS) coupled wavenumber selection chemometric method. Herein, core-shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as SERS substrate, and rhodamine 6G (R6G) was used as signaling probe for Hg 2+ . In the presence of Hg 2+ , citrate ion of Au@Ag NPs induced complexation and become amalgam causes desorption of R6G occurred, resulted in decreased SERS signal intensity. Compared to surface Plasmon resonance method, SERS coupled genetic algorithm-partial least squares realized good correlation coefficient (0.9745 and 0.9773) in their prediction over the concentration ranges 1.0 × 10 2 to 1.0 × 10 -3  µg/g. The recovery (88.45 - 94.73%) and precision (coefficient of variations, 3.28 - 5.76%) exhibiting satisfactory results suggested that the proposed method could be employed to predict Hg 2+ in fish and water samples towards quality and safety monitoring., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Current work proposed a novel quantitative method of volatile aldehydes (VAs) using chemoselective response dyes (CRDs) combined with multivariate data analysis. Multivariate spectral data of selected CRDs was obtained by visible near-infrared spectroscopy. The Synergy-interval Partial Least Squares (Si-PLS) algorithm processed multivariate spectral data to establish VAs quantitative prediction models at the level of 0.0002 v/v to 0.18 v/v. The prediction coefficient (Rp) values of models ranged from 0.8399 to 0.9886, and the Root Mean Square Error of Prediction (RMSEP) values were less than 0.01. These models were verified by classification of aging rice samples, and 93% samples were correctly identified in prediction set. In addition, Density Functional Theory (DFT) calculations explored the interaction mechanism between selected CRDs and VAs. The optimized Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital (HOMO-LUMO) energy levels, dipole moment, distance between molecules were found to have strong correlations with the interaction., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Surface-enhanced Raman spectroscopy (SERS) has become a growing ultrasensitive analytical technique to quantify toxic molecules in foodstuffs. Monitoring the levels of chemical contaminants not only ensures food security but also offers a guideline on the production, processing, and risk analysis of consumer's health protection. The objective of this study was to point out the possible challenges associated with the detection of mycotoxins in foodstuffs. Herein, we have discussed briefly as to selectivity, accuracy, precision, robustness, ruggedness, non-specific adsorption (NSA), cross-reactivity (for both label-free and the target analyte capture approaches like the application of antibody, aptamer, molecularly imprinted polymer (MIP), linear polymer affinity agents and/or specific surface-modified nanomaterials) and their potential solution., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10 8 was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10 -4 to 1.0 × 10 3 µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10 -4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 °C of 1.39 × 10 6 to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 × 10 -4 , 1.88 × 10 -4 and 4.72 × 10 -3 µg/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Noble bimetallic and trimetallic nanoparticles (NBT-NPs) have superior biomedical applications as compared to their monometallic counterparts. The performance of these nanomaterials depends on their composition, shape and size. Hence, the controlled-synthesis of these nanomaterials is a hot area of research. Till date, no review article in the literature accounts regarding the controlled-synthesis and biomedical applications related to morphology, optimum composition, biocompatibility and versatile chemistry of NBT-NPs. Taking this into contemplation, an effort was made to provide a clear insight into the morphology-controlled synthesis and size/shape-dependent anticancer and bactericidal applications of NBT-NPs. Chemical reduction method for the controlled-synthesis of NBT-NPs is reviewed critically. Furthermore, the potential role of various reaction parameters such as time, reducing agents, stabilizing/capping agents, nature/concentration of precursors, temperature and pH in the shape/size-controlled synthesis of these nanomaterials are discussed. In the second part of this article, anticancer and bactericidal applications of the NBT-NPs are reviewed and the influences of optimum composition, size, surface structure, versatile chemistry and synergism are studied. Finally, the current challenges in the controlled-synthesis and biomedical applications of these nanomaterials, and prospects to resolve related issues are discussed. HighlightsChemical reduction method for the synthesis of NBT-NPs is reviewed.The influences of parameters on the control synthesis of NBT-NPs are discussed.Antibacterial and anticancer applications and cytotoxicity of NBT-NPs are reviewed.Possible solutions for the key challenges are discussed.Outlooks about the synthesis and biomedical applications of NBT-NPs are discussed.

A low cost hand-held spectral analytical system was developed for in situ screening of phenolics and flavonoids in puff dried Ziziphus jujuba (Z. Jujuba) samples. Standards of gallic acid, caffeic acid, l-epicatechin, phloridzin and cianidanol were used to quantify the individual phenolics and flavonoids using high performance liquid chromatography-diode array detector (HPLC-DAD). The synergy interval partial least square with ant colony optimization (Si-ACO-PLS) was attempted to optimize and capture informative variables for the prediction of target compounds. The model performance was evaluated using correlation coefficients of prediction (Rp); root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). The Si-ACO-PLS yielded optimal performance, 0.8540 ≤ Rc ≤ 0.9250, 0.8360 ≤ Rp ≤ 0.9056, 0.84 ≤ RMSEP ≤ 16.30 and 2.03 ≤ RPD ≤ 2.26. The hand-held spectral analytical system coupled with Si-ACO-PLS proved to the reliable, rapid and cost-effective method to quantify the phenolics and flavonoids in Z. Jujuba., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Malathion is an organophosphorus pesticide which could remain in agricultural products and exert irreversible harmful effects on human health. Hence, strict monitoring of malathion contents is very significant. Here, a highly sensitive fluorescent aptasensor was developed for the determination of malathion, the system was based on a cationic polymer-mediated fluorescence 'turn-off'. In this system, malathion-specific aptamers were bound to cationic polymer through electrostatic interactions. To produce fluorescence resonance energy transfer (FRET), negatively charged upconversion fluorescent nanoparticles (UCNPs) and cationic-polymer encapsulated gold nanoparticles (GNPs) were combined. This combination resulted in fluorescence quenching, and the degree of quenching was correlated with the concentration of malathion. Under optimum conditions, the fluorescence intensities were observed to decrease linearly with the rising concentration of the malathion from 0.01 to 1 μM with a detection limit of 1.42 nM. Furthermore, the developed sensor possessed good selective recognition ability for malathion and was successfully used to detect malathion in adulterated tap water and matcha samples with high accuracy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The current study explores the first full mode liquid microextraction technique coupled with surface-enhanced Raman spectroscopy (SERS), and has been successfully applied for chromium speciation in food and environmental matrices. Herein, chromium as chlorochromate anion [CrO 3 Cl] - and the cationic rhodamine 6G [RG] + dye has been extracted in organic phase as a complex ion associate [RG + .CrO 3 Cl - .nS] org at pH ≤ 1.0. Afterwards, the extracted phase was deposited on the surface of the nano-flower shaped silver nanoparticles substrate and the SERS response was monitored against the reagent blank at 1505 cm -1 . Substrate characterizations, reaction mechanism assignment, stoichiometry, speciation, analytical applications, selectivity and validation were performed. The analytical procedure exhibits a detection limit of 0.03 µg L -1 under the optimized experimental conditions. The accuracy of the proposed strategy was validated by inductively coupled plasma optical emission spectrometry method using student's t- and F tests at 95% confidence., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This paper demonstrates the application of surface-enhanced Raman scattering (SERS) using positive charged gold nanorods (Au NRs) as an enhancement substrate to classify Pseudomonas spp. coupled with multivariate methods. Four species of Pseudomonas as dominant spoilage bacteria of food were isolated from rotten chicken, namely, Pseudomonas gessardii (P9), Pseudomonas psychrophila (P8), Pseudomonas psychrophila (S2) and Pseudomonas fluorescens (T3). Au NRs were synthesized with positive charge by seed-mediated growth method which can be adsorbed onto the surface of the bacteria by electrostatic adsorption. SERS spectra were collected individually for four types of Pseudomonas and pretreated by mean centering (MC), then principal component analysis (PCA) and hierarchical clustering analysis (LDA) were used to achieve data dimensionality reduction and visualize the result of differentiation for the species of Pseudomonas. Particularly, the classification accuracy of LDA was reached to 100%. Following we applied hierarchical clustering analysis (HCA) to cluster each species of Pseudomonas and the results of HCA consistent with the results of 16S rRNA. This study has shown that SERS combined with LDA and HCA can be used as a reliable method to classify Pseudomonas., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality., Results: Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1 st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (R P ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade., Conclusion: The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

Introduction: Polyphenols are the foremost measure of phytochemicals in Chinese dates due to their many potential health benefits such as averting cancers, reducing the risk of coronary artery disease, diuretic activity, myocardial stimulant, coronary dilator and muscle relaxant., Objective: To quantitate the polyphenols in Chinese dates using a data fusion approach with near-infrared (NIR) and mid-infrared (MIR) spectroscopy., Material and Methods: A total of 80 Chinese dates samples were used for data acquisition from both NIR and MIR spectroscopy. The efficient spectral intervals were extracted by the synergy interval partial least square (Si-PLS) algorithm as input variables for NIR-MIR fusion model. A genetic algorithm (GA) was used to construct the model based on NIR-MIR fusion. The performance of the developed models was evaluated using correlation coefficients of calibration (R 2 ) and prediction (r 2 ), root mean square error of prediction (RMSEP), bias and residual prediction deviation (RPD)., Results: The data fusion model based on the GA was superior compared to NIR and MIR build model. The optimal GA-fusion model yielded R 2  = 0.9621, r 2  = 0.9451, RPD = 2.44, calibration set bias = 0.004 and prediction set bias = 0.061, computing only 15 variables., Conclusion: These findings reveal that integration of NIR and MIR is possible for the prediction of total polyphenol content in Chinese dates., (© 2019 John Wiley & Sons, Ltd.)

This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 10 3 to 1.0 × 10 -4  μg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Nowadays, nano hybrid materials (NHMs) with latent applications have been employed in different fields, particularly for sensor applications. Among NHMs, GO based - upconversion NHMs system is an emerging area for the rapid detection of different hazardous materials either as an aptamer based or free-labeled sensing techniques. For the detection of Pb(II) in water, NaYF 4 @Yb,Ho,Au/GO-NaYF 4 @Yb,Ho,Au NHMs system was developed. The synthesized NHMs were characterized by XRD, FT-IR, SEM with EDS, TEM and Raman characterization techniques for observation and confirmation. NaYF 4 @Yb,Ho,Au/GO-NaYF 4 @Yb,Ho,Au NHMs fabricated sensors were observed to detect and quantify on real-time basis Pb(II) via surface-enhanced-Raman spectroscopy within the dynamic linear range of 98-99%, with detection limits of 1.16 × 10 -9 g/mL and 1.15 × 10 -8 g/mL obtained respectively for NaYF 4 @Yb,Ho,Au and GO- NaYF 4 @Yb,Ho,Au NHMs. The relative standard deviation (RSD) value achieved for both were less than ∼10%, indicative of reproducibility in the quantification results for Pb(II) traces in water when combined with genetic algorithm partial least square (GA-PLS). Results suggest that the GO-NHMs better enhanced the SERS Pb (II) vis-à-vis NaYF 4 @Yb,Ho,Au. The GO-wrapped NHMs exhibited a further better SERS performance because the heterostructure of GO-NHMs could be potentially useful for SERS-based immunoassay due to the much easier charge transfer between graphene and the metal ions and molecules so the homogeneity of the SERS probe was improved simultaneously (chemical enhancement) by GO-NHMs., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Pesticide residue in food is of grave concern in recent years. In this paper, a rapid, sensitive, SERS (Surface-enhanced Raman scattering) active reduced-graphene-oxide-gold-nano-star (rGO-NS) nano-composite nanosensor was developed for the detection of acetamiprid (AC) residue in green tea. Different concentrations of AC combined with rGO-NS nano-composite electro-statically, yielded a strong SERS signal linearly with increasing concentration of AC ranging from 1.0 × 10 -4 to 1.0 × 10 3  μg/mL indicating the potential of rGO-NS nano-composite to detect AC in green tea. Genetic algorithm-partial least squares regression (GA-PLS) algorithm was used to develop a quantitative model for AC residue prediction. The GA-PLS model achieved a correlation coefficient (Rc) of 0.9772 and recovery of the real sample of 97.06%-115.88% and RSD of 5.98% using the developed method. The overall results demonstrated that Raman spectroscopy combined with SERS active rGO-NS nano-composite could be utilized to determine AC residue in green tea to achieve quality and safety., (Copyright © 2018. Published by Elsevier Taiwan LLC.)

A convenient and novel method of cyclisation of 4-(4-substituted-phenylsulfonamido)-butanoic acids to their corresponding p-substituted 1-arylsulfonyl-pyrrolidin-2-ones was achieved by using polyphosphate ester (PPE). The reaction times were considerably reduced, with an increase in yields, when PPE was used in combination with a catalytic amount of pyridine.

In the title compound, C(15)H(14)N(4)O(2)S, the phenyl ring is inclined at 70.25 (6)° with respect to the approximately planar fur-yl-triazolsulfan-yl-acetamide unit. In the crystal structure, mol-ecules related by inversion centers form dimers via inter-molecular N-H⋯O hydrogen bonds between acetamide groups, resulting in eight-membered rings with an R(2) (2)(8) motif. In addition, the other H atom of the acetamide group is involved in an inter-molecular hydrogen bond with an N atom of the triazole ring, resulting in chains extended along the c axis. The overall effect is the formation of a hydrogen-bonded two-dimensional framework perpendicular to the a axis.

In the crystal structure of the title compound, C(14)H(11)FN(2)O(2), the molecule is centrosymmetric. The F atom is disordered over four positions, on the two ortho positions of each ring, with occupancies of 0.287:0.213 (5). In the crystal structure, mol-ecules are linked by inter-molecular N-H⋯O and C-H⋯O hydrogen bonds.

In the asymmetric unit of the title compound, C(13)H(11)N(3)OS·0.5H(2)O, there are two independent mol-ecules of 4-benzyl-3-(2-fur-yl)-1H-1,2,4-triazole-5(4H)-thione and a water mol-ecule of hydration. The conformation of the two organic mol-ecules is slightly different, the dihedral angles formed by the furyl and triazole rings being 5.63 (15) and 17.66 (13)°. The water mol-ecule of hydration links three adjacent triazole mol-ecules to form a cluster via inter-molecular O-H⋯S, N-H⋯S and N-H⋯O hydrogen bonds, generating a 10-membered ring of graph set R(3) (3)(10). The crystal structure is further stabilized by intra- and inter-molecular C-H⋯S, C-H⋯O and C-H⋯N hydrogen bonds and by π-π stacking inter-actions involving the furyl and triazole rings of centrosymmetrically related mol-ecules, with a centroid-centroid separation of 3.470 (2) Å.

The title compound, C(9)H(10)N(4)S·0.095H(2)O, consists of discrete 4-ethyl-3-(3-pyrid-yl)-1H-1,2,4-triazole-5(4H)-thione mol-ecules and a disordered mol-ecule of water of hydration with partial occupancy, lying on a twofold rotation axis. The dihedral angle between the pyridine and triazole rings is 41.73 (8)°. In the crystal structure, mol-ecules are hydrogen bonded via triazole NH groups and pyridyl N atoms, forming chains parallel to the a axis.

The title mol-ecule, C(12)H(12)N(2)O(3)S, is composed of two individually planar units, viz. 5-phenyl-1,3,4-oxadiazol-2-yl-sulfanyl and ethyl acetate, which are oriented at almost right angles [80.07 (8)°] with respect to each other. The structure is stabilized by weak inter-molecular C-H⋯O and C-H⋯N hydrogen bonds. The phenyl and oxadiazole rings show π-π stacking inter-actions [centroid-centroid distance = 3.846 (2) Å] and there is also a short π-inter-action between the carbonyl O atom and the oxadiazole ring [the distance from this O atom to the centroid of the oxadiazole ring is 3.156 (2) Å].

Novel cyclization of 4-(substituted-phenylsulfonamido)butanoic acids to their corresponding 1-[(substituted-phenyl)sulfonyl]pyrrolidin-2-ones was successfully achieved by using polyphosphate ester (PPE). The reaction time was considerably reduced with corresponding increase in the yields, when polyphosphate ester (PPE) was used in combination with 4-(N,N-dimethylamino)pyridine (DMAP). All the synthesized compounds were screened for their antimicrobial activity. Minimum Inhibitory Concentration (MIC) values of synthesized compounds were also determined, and were found to be in the range of 0.09-1.0 mg.

A series of new benzenesulfonamides, most of which are chiral, incorporating 1, 3, 4-oxadiazole and amino acid moieties have been synthesized. Some of these compounds were screened for antimalarial activity and also evaluated for their ability to inhibit hem polymerization. The electrophoretic analysis indicated that one compound was effective in inhibiting the degradation of hemoglobin. The synthesized compounds were tested in mice infected with Plasmodium berghei. These derivatives have the potential for the development of novel antimalarial lead compounds.

A series of chiral 1,3,4-oxadiazole-5-thiols incorporating 2-substituted-benzenesulfonamide moieties has been prepared from amino acids, via the ester and carbohydrazide intermediate, followed by cyclization with carbon disulfide. Some of these compounds have been investigated for the inhibition of three physiologically relevant carbonic anhydrase (CA, EC 4.2.1.1) isoforms, the human cytosolic hCA I and II, and the human, transmembrane, tumor-associated isozyme hCA IX. All these compounds showed weak (millimolar) affinity for the three isozymes, except two carbohydrazides and two heterocyclic thiols which selectively inhibited the tumor-associated isozyme with inhibition constants around 10 microM. Such compounds constitute interesting lead molecules for the possible design of CA IX-selective inhibitors.