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Pork batter quality significantly affects its product. Herein, this study explored the use of Raman spectroscopy combined with deep learning algorithms for rapidly detecting pork batter quality and revealing the mechanisms of quality changes during heating. Results showed that heating increased β-sheet content (from 26.38 to 41.42%) and exposed hidden hydrophobic groups, which formed aggregates through chemical bonds. Dominant hydrophobic interactions further cross-linked these aggregates, establishing a more homogeneous and denser network at 80 °C. Subsequently, convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and CNN-LSTM were comparatively used to predict gel strength and whiteness in batters based on the Raman spectrum. Thereinto, CNN-LSTM provided the optimal results for gel strength (Rp = 0.9515, RPD = 3.1513) and whiteness (Rp = 0.9383, RPD = 3.0152). Therefore, this study demonstrated the potential of Raman spectroscopy combined with deep learning algorithms as non-destructive tools for predicting pork batter quality and elucidating quality change mechanisms., 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 Ltd. All rights reserved.)

The expansive potential of surface-enhanced Raman scattering (SERS) has been well-established; however, the primary bottleneck hindering its routine analytical and commercial implementation is the poor signal reproducibility and challenges in substrate fabrication. Thus, the current work attempts to synthesize a scalable and reproducible nanoporous gold (npAu) decorated with gold (Au) nanoparticles to generate a highly structured Au@npAu nanocomposite. The substrate fabrication completes via three distinct routes: i) selective dealloying to form npAu on the Au film, ii) the fast deposition (i-t = -0.8 V, t = 10.0 s) of Au atoms across the npAu surface, and finally iii) the precise growth control of the generated Au@npAu by a series of by oxidation-reduction cycles (-0.03 to -0.4 V for 80.0 segments at ν = 50.0 mVs -1 ). The simulations of the dealloyed npAu and the final Au@npAu nanocomposite showed that the reduced interparticle spacing and ligament size in the Au@npAu nanocomposite is crucial for forming abundant "hot spot" regions with highly concentrated electromagnetic fields. The Au@npAu substrate reproducibility was assessed on 400.0 sites for SERS spectral acquisition with a relative standard deviation of 9.22 %. Furthermore, the Au@npAu was checked under different preparation batches for intra- and inter-day analysis and storage for 20.0 days with good stability. Finally, the substrate was checked for direct SERS detection of ferbam residues with a 4.34 × 10 -9  mol L -1 sensitivity and examined in real samples with satisfactory recoveries (97.63 ± 1.95%-99.16 ± 0.24 %). This work offers a promising avenue towards highly reproducible, scalable and universal Au@npAu SERS substrate fabrication in diverse SERS-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.)

An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10 -3  mg/L, respectively., 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. Published by Elsevier Ltd.)

Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong 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 © 2024 Elsevier Ltd. All rights reserved.)

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R 2  = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples., 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 Ltd. All rights reserved.)

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.)

Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0-50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp 2  = 0.8772) with the ELM model, demonstrating potential for rapid, non-destructive prediction of minced chicken gel strength quality., Competing Interests: Declaration of competing interest The authors disclose no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Artificial intelligence (AI) technology is advancing the digitization and intelligence development of the food industry. A promising application is using deep learning-assisted visible near-infrared (vis-NIR) spectroscopy to monitor residual sugar and bacterial concentration in real-time, ensuring kombucha quality during production. The feature fingerprints of residual sugar and bacterial concentration were extracted by four variable selection algorithms and then reconstructed using serial and parallel processing methods. Based on these reconstructed features, Partial Least Squares (PLS) and Convolutional Neural Networks (1DCNN and 2DCNN) models were developed and compared. The experimental results showed that the 2DCNN model based on reconstruction features achieved superior performance. The RPDs of the residual sugar and bacterial concentrations models were 4.49 and 6.88, while the MAEs were 0.42 mg/mL and 0.04 (Abs), respectively. These results suggest that the proposed modeling strategy effectively supports quality control during kombucha production and provides a new perspective for spectral analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have potentially influenced the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 10 6 . The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10 -5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia., 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.)

The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (R p ), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (R p  = 0.933, RPD = 3.63), chlorogenic acid (R p  = 0.928, RPD = 3.52), p-coumaric acid (R p  = 0.900, RPD = 2.37), and stigmasterol (R p  = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage., 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.)

A novel method was developed for the early detection of wheat infected with Aspergillus flavus (A. flavus) using a nanocomposite colorimetric sensors array (CSA). LC-MS analysis revealed the presence of Aflatoxin B1 (AFB1) and Aflatoxin B2 (AFB2) on day seven, demonstrating mycotoxin variabilities in infected wheat. HS-SPME-GC-MS analysis detected 2-methylbutyral, a gas exclusively associated with toxigenic A. flavus. The CSA was modified using three nanoparticles of MOF and successfully used to detect the wheat infected with A. flavus. Discrimination of different types of infected wheat samples was achieved using the RGB difference map and Principal Component Analysis (PCA) model. Additionally, the Linear Discriminant Analysis (LDA) model accurately predicted the presence of toxigenic A. flavus at various stages of infection. These findings highlight the promising capabilities of nanocomposite CSA for early-stage detection of A. flavus infection in wheat., 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.)

Producing starch gels with superior mechanical attributes remains a challenging pursuit. This research sought to develop a simple method using ethanol exposure to produce robust starch gels. The gels' mechanical properties, rheology, structural characteristics, and digestion were assessed through textural, rheological, structural, and in vitro digestion analyses. Our investigation revealed an improvement in the gel's strength from 62.22 to178.82 g. The thermal transitions were accelerated when ethanol was elevated. The exposure to ethanol resulted in a reduction in syneresis from 11% to 9.5% over a period of 6 h, with noticeable changes in size and color. Rheologically, the dominating storage modulus and tan delta (<0.55) emphasized the gel's improved elasticity. X-ray analysis showed stable B- and V-type patterns after ethanol exposure, with relative crystallinity increasing to 7.9%. Digestibility revealed an ethanol-induced resistance, with resistant starch increasing from 1.87 to 8.73%. In general, the exposure to ethanol played a crucial role in enhancing the mechanical characteristics of kudzu starch gels while simultaneously preserving higher levels of resistant starch fractions. These findings have wide-ranging implications in the fields of confectioneries, desserts, beverages, and pharmaceuticals, underscoring the extensive academic and industrial importance of this study.

Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R 2 p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes., 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.)

Volatile organic compounds (VOCs) are an important indicator for fungal-infected wheat identification. This work proposes a novel approach for toxigenic Aspergillus flavus infected wheat identification through characteristic VOCs analyzed by nano-composite colorimetric sensors. Nanoparticles of poly styrene-co-acrylic acid (PSA), porous silica nanoparticles (PSN), and metal-organic framework (MOF) were combined with boron dipyrromethene (BODIPY) to fabricate nano-composite colorimetric sensors. The combination mechanisms for nanoparticles and the information extracted from nano-colorimetric sensors by digital images were analyzed in the current work. Furthermore, linear discriminant analysis (LDA) and k-nearest neighbor (KNN) were used comparatively to analyze the data from images, and toxigenic Aspergillus flavus infected wheat samples could be 100.00% correctly identified when using the optimal KNN model. This research contributes to the practical analysis of VOCs and the detection of toxigenic Aspergillus flavus infected wheat., 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 Ltd. All rights reserved.)

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.