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.

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.

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.

Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry.

Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R2p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.

Grading and Sorting of Grape Berries Using Visible-Near Infrared Spectroscopy on the Basis of Multiple Inner Quality Parameters.

The potential of visible-near infrared (vis/NIR) spectroscopy (400 nm to 1100 nm) for classification of grape berries on the basis of multi inner quality parameters was investigated. Stored Vitis vinifera L. cv. Manicure Finger and Vitis vinifera L. cv. Ugni Blanc grape berries were separated into three classes based on the distribution of total soluble solid content (SSC) and total phenolic compounds (TP). Partial least squares regression (PLS) was applied to predict the quality parameters, including color space CIELAB, SSC, and TP. The prediction results showed that the vis/NIR spectrum correlated with the SSC and TP present in the intact grape berries with determination coefficient of prediction (RP2) in the range of 0.735 to 0.823. Next, the vis/NIR spectrum was used to distinguish between berries with different SSC and TP concentrations using partial least squares discrimination analysis (PLS-DA) with >77% accuracy. This study provides a method to identify stored grape quality classes based on the spectroscopy and distributions of multiple inner quality parameters.

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis.

Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Hot air treatment reduces postharvest decay and delays softening of cherry tomato by regulating gene expression and activities of cell wall-degrading enzymes.

BACKGROUND: Fruit softening facilitates pathogen infection and postharvest decay, leading to the reduction of shelf-life. Hot air (HA) treatment at 38 °C for 12 h is effective in reducing postharvest disease and chilling injury of tomato fruit. To explore the effect and mechanism of HA treatment on reducing postharvest decay and softening of cherry tomato, fruit at the mature green stage were treated with HA and then stored at 20 °C for 15 days. Changes in natural decay incidence, firmness, cell wall compositions, activities and gene expression of cell wall-degrading enzymes of cherry tomatoes were assessed. RESULTS: HA treatment reduced natural decay incidence, postponed the firmness decline, inhibited the respiration rate and ethylene production, and retarded pectin solubilisation and cellulose degradation of cherry tomatoes. Enzymatic activities and gene expression of pectin methylesterase, polygalacturonase, cellulase and ß-galactosidase were inhibited by HA treatment. In addition, the gene expression of LeEXP1 was reduced, while LeEXT was up-regulated after HA treatment. CONCLUSIONS: Our findings suggested that HA treatment could inhibit cell wall degradation and postpone softening of cherry tomatoes by regulating gene expression and activities of cell wall-degrading enzymes, resulting in the reduction of postharvest decay. © 2017 Society of Chemical Industry.

Rapid detection of adulterated peony seed oil by electronic nose.

Peony seed oil has recently been introduced as a high-quality food oil. Because the high price of peony seed oil may tempt unscrupulous merchants to dilute it with cheaper substitutes, a rapid detection method for likely adulterants is required. In this study, the fatty acid composition of peony seed oil and four less expensive edible oils (soybean oil, corn oil, sunflower oil, and rapeseed oil) were measured by gas chromatography mass spectrometry. Peony oil adulterated by other edible oils was assessed using iodine values to estimate the extent of adulteration. Adulteration was also measured using an electronic nose (E-nose) combined with principal component analysis (PCA) or linear discriminant analysis (LDA). Results indicated that peony seed oil was highly enriched in α-linolenic acid. Although the iodine value can be used to detect some adulterants by measuring unsaturation, it was not able to detect all four potential adulterants. In contrast, the E-nose can rapidly identify adulterated peony seed oil by sampling vapor. Data analyses using PCA and LDA show that LDA more effectively clusters the data, discriminates between pure and adulterated oil, and can detect adulteration at the 10% level. E-nose combined with LDA suitable for detection of peony seed oil adulteration.

Comparison of Benchtop Fourier-Transform (FT) and Portable Grating Scanning Spectrometers for Determination of Total Soluble Solid Contents in Single Grape Berry (Vitis vinifera L.) and Calibration Transfer.

Near-infrared (NIR) spectroscopy was applied for the determination of total soluble solid contents (SSC) of single Ruby Seedless grape berries using both benchtop Fourier transform (VECTOR 22/N) and portable grating scanning (SupNIR-1500) spectrometers in this study. The results showed that the best SSC prediction was obtained by VECTOR 22/N in the range of 12,000 to 4000 cm-1 (833-2500 nm) for Ruby Seedless with determination coefficient of prediction (Rp²) of 0.918, root mean squares error of prediction (RMSEP) of 0.758% based on least squares support vector machine (LS-SVM). Calibration transfer was conducted on the same spectral range of two instruments (1000-1800 nm) based on the LS-SVM model. By conducting Kennard-Stone (KS) to divide sample sets, selecting the optimal number of standardization samples and applying Passing-Bablok regression to choose the optimal instrument as the master instrument, a modified calibration transfer method between two spectrometers was developed. When 45 samples were selected for the standardization set, the linear interpolation-piecewise direct standardization (linear interpolation-PDS) performed well for calibration transfer with Rp² of 0.857 and RMSEP of 1.099% in the spectral region of 1000-1800 nm. And it was proved that re-calculating the standardization samples into master model could improve the performance of calibration transfer in this study. This work indicated that NIR could be used as a rapid and non-destructive method for SSC prediction, and provided a feasibility to solve the transfer difficulty between totally different NIR spectrometers.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.