Assessment of optical properties and Monte-Carlo based simulation of light propagation in citrus infected by Penicillium italicum.

This original work investigated the optical properties and Monte-Carlo (MC) based simulation of light propagation in the flavedo of Nanfeng tangerine (NF) and Gannan navel orange (GN) infected by Penicillium italicum. The increase of absorption coefficient (µa) at around 482 nm and the decrease at around 675 nm were both observed in infected NF and GN during storage, indicating the accumulation of carotenoids and loss of chlorophyll. Particularly, the µa in NF varied more intensively than GN, but the limited differences of reduced scattering coefficient (µs') were detected while postharvest infection. Besides, MC simulation of light propagation indicated that the photon packets weight and penetration depth at 482 nm in NF were reduced more than in GN flavedo, while there were almost no changes at the relatively low absorption wavelength of 926 nm. The simulated absorption energy at 482 nm in NF and GN presented more changes than those at 675 nm during infection, thus could provide better detection of citrus diseases. Furthermore, PLS-DA models can discriminate healthy and infected citrus, with the accuracy of 95.24 % for NF and 98.67 % for GN, respectively. Consequently, these results can provide theoretical fundamentals to improve modelling prediction robustness and accuracy.

Storage Properties and Shelf-Life Prediction of Fresh-Cut Radishes Treated by Photodynamic Technology.

Fresh-cut radishes are susceptible to quality loss and microbial contamination during storage, resulting in a short shelf life. This study investigated the effects of photodynamic technology (PDT) on fresh-cut radishes stored at 4 °C for 10 d and developed appropriate models to predict the shelf life. Results showed that curcumin-mediated PDT maintained sensory acceptability, color, and firmness, decreased weight loss, and increased ascorbic acid and total phenolics of samples by inactivating polyphenol oxidase and peroxidase, resulting in improved antioxidant capacity and quality. The total bacteria count in samples was significantly (p < 0.05) reduced by 2.01 log CFU g-1 after PDT and their shelf life was extended by 6 d compared to the control. To accurately predict the shelf life, the kinetic models based on microbial growth were established, while weight loss, b* value, firmness, and ascorbic acid were selected as representative attributes for developing quality-based prediction models through correlation analysis. Modeling results showed prediction models based on ascorbic acid best fitted PDT-treated samples, while the modified Gompertz model based on bacteria growth was the best for control and samples treated by sodium hypochlorite. This study suggests that PDT is promising in extending the shelf life of fresh-cut radishes, and using critical indexes to establish the prediction model can provide a more reliable shelf-life estimation.

Non-invasive anticipation of infusion taste in fine-manipulated green teas through hyperspectral appearance analysis guided by ECG content.

The high catechin content in summer-to-autumn tea leaves often results in strong, unpleasant tastes, leading to significant resource waste and economic losses due to lignification of unpicked leaves. This study aims to improve the taste quality of summer-to-autumn green teas by combining fine manipulation techniques with hyperspectral observation. Fine manipulation notably enhanced infusion taste quality, particularly in astringency and its aftertaste (aftertasteA). Using Partial Least Squares Discriminant Analysis (PLSDA) on hyperspectral data, 100% prediction accuracy was achieved for dry tea appearance in the near-infrared spectrum. Astringency and aftertasteA correlated with hyperspectral data, allowing precise estimation with over 90% accuracy in both visible and near-infrared spectrums. Epicatechin gallate (ECG) emerged as a key taste compound, enabling non-invasive taste prediction. Practical applications in processing and quality control are demonstrated by the derived equations (Astringency = -0.88 × ECG + 45.401, AftertasteA = -0.353 × ECG + 18.609), highlighting ECG's role in shaping green tea taste profiles.

Visualizing the structural and chemical heterogeneity of fruit and vegetables using advanced imaging techniques: fundamentals, instrumental aspects, applications and future perspectives.

Fruit and vegetables (F&V) are vastly complicated products with highly diverse chemical and structural characteristics. Advanced imaging techniques either combine imaging with spectral information or can provide excellent tissue penetration, and enable the possibility to target, visualize and even qualify the chemical and physical (structural) heterogeneity within F&V. In this review, visible and/or near infrared hyperspectral imaging, Fourier transform infrared microspectroscopic imaging, Raman imaging, X-ray and magnetic resonance imaging to reveal chemical and structural information in a spatial context of F&V at the macro- (entire products), meso- (tissues), and micro- (individual cells) scales are comprehensively summarized. In addition, their basic concepts and operational procedures, particularly sample preparation and instrumental parameter adjustments, are addressed. Finally, future challenges and perspectives of these techniques are put forward. These imaging techniques are powerful tools to assess the biochemical and structural heterogeneity of F&V. Cost reduction, sensor fusion and data sharing platforms are future trends. More emphasis on aspects of knowledge and extension at the level of academia and research, especially on how to select techniques, choose operational parameters and prepare samples, are important to overcome barriers for the wider adoption of these techniques to improve the evaluation of F&V quality.

Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention.

Polycyclic Aromatic Hydrocarbons' Impact on Crops and Occurrence, Sources, and Detection Methods in Food: A Review.

Polycyclic aromatic hydrocarbons (PAHs) represent a category of persistent organic pollutants that pose a global concern in the realm of food safety due to their recognized carcinogenic properties in humans. Food can be contaminated with PAHs that are present in water, air, or soil, or during food processing and cooking. The wide and varied sources of PAHs contribute to their persistent contamination of food, leading to their accumulation within these products. As a result, monitoring of the levels of PAHs in food is necessary to guarantee the safety of food products as well as the public health. This review paper attempts to give its readers an overview of the impact of PAHs on crops, their occurrence and sources, and the methodologies employed for the sample preparation and detection of PAHs in food. In addition, possible directions for future research are proposed. The objective is to provide references for the monitoring, prevention, and in-depth exploration of PAHs in food.

Introducing high-performance star-shaped bimetallic nanotags into SERS aptasensor: An ultrasensitive and interference-free method for chlorpyrifos detection.

Implementation of cost-effective, reliable, and efficient technologies for the sensitive, rapid, and accurate detection of pesticide residues in agriproducts presents a promising solution to the escalating food safety concerns. Herein, a high-performance surface-enhanced Raman scattering (SERS) aptasensor based on nanotag (AuNS@4-MBN@Ag-aptamer) was introduced for ultrasensitive, reliable, and interference-free detection of chlorpyrifos (CPF). This aptasensor featured star-shaped bimetallic nanotag as the principal Raman signal enhancement material and 4-mercaptobenzonitrile (4-MBN) as "biological-silent"-window reporter (at 2228 cm-1). Moreover, cDNA-linked Fe3O4@AuNPs (FA-cDNA) served as magnetic substrates to simplify the separation process of FA-cDNA-combined nanotags. In the aptasensor, the formation of FA-cDNA-aptamer-AuNS@4-MBN@Ag hybrids was hindered by CPF, and its Raman intensity decreased with increasing CPF concentration. Under optimal SERS conditions, the aptasensor exhibited a broad linear detection range from 2.5 × 102 to 5.0 × 104 pg⋅mL-1, with an impressively low limit of detection of 220.35 pg⋅mL-1 (signal-to-noise ratio = 3). The selectivity and reproducibility assessments highlighted its exceptional sensitivity and interference-free capabilities. Furthermore, practical applications on wheat and apples demonstrated satisfactory spiked recovery rates, ranging from 89.61% to 107.33% (relative standard deviation ≤ 14.55%). Consequently, the high-performance "biological-silent"-window nanotag-based aptasensor is a promising tool for monitoring trace CPF in complex matrices.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Oil addition increases the heat resistance of Clostridium sporogenes spores in braised sauce beef: Perspectives from spore surface characteristics and microstructure.

During thermal processing of braised sauce beef, the lipid content of circularly used sauce increased accordingly because of lipid migration from beef to sauce, which may impact the bacterial heat resistance in the products. This study aims to characterize the heat resistance of Clostridium sporogenes spores in braised sauce beef, and investigate the effects of oil on the spore surface characteristics and microstructure. The results indicated that the heat resistance of C. sporogenes spores in beef was significantly higher than that in sauce. Oil addition remarkably enhanced the spore heat resistance in sauce, with D95°C value three times more than that without oil added, and even higher than that in beef. The results of spore surface characteristics indicated that oil addition led to an increase of hydrophobicity and a decrease of zeta potential, which ultimately increased spore heat resistance. Microstructure analysis indicated that exosporium maintenance and cortex expansion induced by oil addition might contribute to the increase of spore heat resistance. This study has sufficiently verified the importance of oil content on the heat resistance of C. sporogenes spores, which should be taken into consideration when developing thermal processes for controlling the spores in food matrices.

Investigation of 3D printing product of powder-based white mushroom incorporated with soybean protein isolate as dysphagia diet.

The elderly people are prone to dysphagia due to weakened muscle strength. 3D food printing could modify the nutritional ratio and shape design to produce personalized nutritious food suitable for patients with dysphagia. White mushroom (Agaricus bisporus) is rich in a variety of active ingredients such as polysaccharides and polyphenols which are beneficial to human body, but its unique texture is not suitable for patients with dysphagia to chew. This study investigated the impact of different concentrations of soybean protein isolate (SPI, 3%, 5%, 7%, w/w) on 3D food printing of white mushroom powder and carried out the hierarchical representation of dysphagia diet within the framework of International Dysphagia Diet Standardization Initiative (IDDSI). The results illustrated that SPI addition to white mushroom gel reduced water mobility and promoted hydrogen bond formation, which significantly improved the mechanical strength and cohesiveness of printing inks, including yield stress, viscosity and hardness. IDDSI tests showed that the SPI addition of 3% and 5% helped the printing ink pass the spoon tilt test and the fork drip test, which could be classified as level 5 minced and moist food under the consideration of the fork pressure test. The 3D printing results indicated that the 7% SPI addition made the yield stress too high and was not easy for extrusion, resulting in the appearance defects of the printed sample. The addition of 3% SPI could make the printed sample have smooth surface and excellent self-supporting capacity. This work provides insights of white mushroom 3D printing technology as a more visually appealing dysphagia diet.

A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics.

Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.

Correction: Chen et al. Upconversion Fluorescence Nanoprobe-Based FRET for the Sensitive Determination of Shigella. Biosensors 2022, 12, 795.

In the original publication, there is a mistake in Figure 1: A duplication error between 1B and 1C, which occurred due to the similarity of the images of the three nanoparticles [...].

Development of a Dual Mode UCNPs-MB Biosensor in Combination with PCR for Sensitive Detection of Salmonella.

In recent years, the high prevalence of Salmonella has emerged as a serious threat to public safety, prompting attempts to utilize accurate, rapid, and direct methods to ensure food safety. In this study, a multifunctional platform featuring dual-mode detection channels (colorimetric-fluorescence) combined with polymer chain reaction (PCR) was proposed for the sensitive and rapid detection of Salmonella. Additionally, the colorimetric measurements were achieved by color changes induced by methylene blue (MB) insertion into the double-stranded DNA, and the fluorescence measurements were performed by internal filter effect (IFE)-induced fluorescence quenching of upconversion nanoparticles (UCNPs) by MB. The results showed that the IFE and PCR amplification processes improved the sensitivity of the sensor towards Salmonella detection, with a limit of detection (LOD) of 21.8 CFU/mL. Moreover, this colorimetric-fluorescence dual-mode PCR biosensor was applied to determine Salmonella in food samples, such as chicken, egg, and fish, which produced satisfactory results. Overall, the present study results demonstrate the potential for combining PCR amplification with IFE to develop an efficient and reliable dual-mode analysis platform to safeguard food security.

Exploration of molecular interaction between different plant proteins and 2-pentylfuran: based on multiple spectroscopy and molecular docking.

BACKGROUND: Soy protein, peanut protein and wheat protein are commonly applied in plant-based products, but specific off-odor makes it difficult for consumers to accept, with 2-pentylfuran being one of the most representative flavors. In this study, 2-pentylfuran was employed as an example to explore the behavior and mechanism of the three proteins in absorbing off-odors. RESULTS: Gas chromatographic-mass spectrometric analysis indicated that different plant proteins were able to adsorb 2-pentylfuran. Circular dichroism proved 2-pentylfuran could drive the α-helix to ß-sheet transition of soy protein, which was not obvious in peanut protein or wheat protein. Ultraviolet spectroscopy tentatively determined that 2-pentylfuran caused changes in the tyrosine and tryptophan microenvironments of different plant proteins, which were further evidenced by synchronous fluorescence at fixed wavelength intervals of 15 nm and 60 nm. Static quenching of protein intrinsic fluorescence indicated that they formed a stable complex with 2-pentylfuran, except for wheat protein (dynamic quenching). CONCLUSION: The various conformations of the three proteins are the main reason for the difference in flavor retention of protein. Soy protein, peanut protein and wheat protein adsorbing 2-pentylfuran relies on non-covalent forces, especially hydrophobic interactions, maintained between the protein and 2-pentylfuran. © 2023 Society of Chemical Industry.

Influence of maturity on bruise detection of peach by structured multispectral imaging.

Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.

Study on Black Spot Disease Detection and Pathogenic Process Visualization on Winter Jujubes Using Hyperspectral Imaging System.

In this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.

Polysaccharides from Nitraria retusa Fruit: Extraction, Purification, Structural Characterization, and Antioxidant Activities.

Polysaccharides are important bioactive components of Nitraria retusa fruit (NRF). In this study, the ultrasonic-assisted extraction (UAE) conditions of polysaccharides from Nitraria retusa fruit (NRFPs) were optimized by response surface methodology (RSM). The structural characteristics and antioxidant activity were investigated. The maximum NRFPs yield of 3.35% was obtained under the following optimal conditions: temperature of 59.5 °C, time of 30.5 min, liquid-to-solid ratio of 19.5 mL/g. Three polysaccharide fractions, NRFP-1 (20.01 kDa), NRFP-2 (28.96 kDa), and NRFP-3 (67.45 kDa), were isolated. Glucose, galactose, and arabinose in different percentages were identified as the primary monosaccharide units. The Fourier transform infrared spectrometer (FT-IR) and nuclear magnetic resonance (NMR) analysis indicated the presence of α- and ß-glycosidic bonds in NRFPs. The NRFP-3 exhibited the highest scavenging activities against DPPH, ABTS, -OH free radicals, and Fe+3-reducing activity.

A novel ADA-coated UCNPs@NB sensing platform combined with nucleic acid amplification for rapid detection of Escherichia coli.

In this study, we reported a novel sensing platform based on fluorescence quenching composed of alendronic acid (ADA) coated upconversion nanoparticles (UCNPs) and Nile Blue (NB) combined with polymerase chain reaction (PCR) for rapid, sensitive, and specific detection of Escherichia coli (E. coli). As a fluorescence acceptor, NB has a broad absorption band and can quench upconversion fluorescence intensity at 544 nm and 658 nm based on IFE. PCR is a double-stranded DNA (dsDNA) amplification technique with high specificity. The NB-dsDNA complex can be formed by intercalation of NB between base pairs and groove of dsDNA, leading to upconversion fluorescence recovery. The ADA-coated UCNPs@NB sensing platform achieved to detect E. coli in 1.5 h, with a lower limit of detection (33 CFU mL-1). In addition, the sensitivity of the ADA@UCNPs-NB fluorescence sensor under different PCR cycle numbers was discussed. The results showed that the proposed sensor could effectively shorten the assay time (1.0 h) while maintaining excellent sensitivity. This study demonstrated a rapid and sensitive analytical method for detecting E. coli in chicken, providing a reference for constructing PCR fluorescence sensors.

Assessment of the optical properties with physicochemical properties and cell wall polysaccharides of 'Korla' pear flesh during Alternaria alternata-induced disease development.

Cell wall polysaccharides and physicochemical properties are the major quality characteristics of fruit, but they are significantly affected by the postharvest disease. In this study, the influence of Alternaria alternata-induced disease on the contents of cell wall polysaccharides and physicochemical properties in 'Korla' pear flesh during storage, as well as their relationships of the optical absorption (µa) and reduced scattering (µs') were explored. The infected pear had lower individual sugars, covalent-soluble pectin, cellulose and hemicellulose contents than the healthy ones. The successive decreases of µa and increases of µs' in pears were observed while the process of pathogen infection. Path-coefficient analysis indicated the ionic-soluble pectin was the main reason responsible for the change of µs' in infected pear at 675 nm and 980 nm. This study indicated the optical properties have the possibility to present the physicochemical characteristics and cell wall polysaccharides of pears during postharvest pathogen infection.

Detection of Alternaria alternata infection in winter jujubes based on optical properties and their correlation with internal quality parameters during storage.

In this work, a single integrating sphere system was applied to characterize the optical absorption (µa) and reduced scattering (µs') properties (550 - 1050 nm) in winter jujube flesh infected by Alternaria alternata during storage at 4 and 20 °C, respectively. Meanwhile, physical (L*, a*, weight loss) and biochemical characteristics (soluble solids content, titratable acids, chlorophyll, total phenolic, and ascorbic acid) of winter jujubes were measured. Among them, chlorophyll, weight loss and ascorbic acid were highly correlated with µa at 680 nm, 690 nm, while chlorophyll and a* had the best correlations with µs' at 700 - 920 nm. These optimal optical properties were proved efficiently contributed to the disease detection of winter jujubes after 12 days at 4 °C and 3 days at 20 °C during storage, with satisfactory discrimination accuracies (acc > 93.75 %). Consequently, optical properties in Vis-NIR region were available to detect the postharvest disease in winter jujubes.

Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue.

The growth models of total bacterial count in freshly squeezed strawberry juice were established by gas and taste sensors in this paper. By selecting the optimal sensors and fusing the response values, the Modified Gompertz, Logistic, Huang and Baranyi models were used to predict and simulate the growth of bacteria. The results showed that the R2 values for fitting the growth model of total bacterial count of the sensor S7 (an electronic nose sensor), of sweetness and of the principal components scores were 0.890-0.944, 0.861-0.885 and 0.954-0.964, respectively. The correlation coefficients, or R-values, between models fitted by the response values and total bacterial count ranged from 0.815 to 0.999. A single system of electronic nose (E-nose) or electronic tongue (E-tongue) sensors could be used to predict the total bacterial count in freshly squeezed strawberry juice during cold storage, while the higher rate was gained by the combination of these two systems. The fusion of E-nose and E-tongue had the best fitting-precision in predicting the total bacterial count in freshly squeezed strawberry juice during cold storage. This study proved that it was feasible to predict the growth of bacteria in freshly squeezed strawberry juice using E-nose and E-tongue sensors.