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This study aimed to investigate the effects of a combination of acidic electrolyzed water (AEW) and modified atmosphere packaging (MAP) on the quality of jujube stored at 1 ± 0.5°C for 40 days. Jujube was treated with AEW + MAP, AEW, and MAP, respectively, to identify an appropriate preservation method based on the changes of quality indicators, activities of antioxidant enzymes and nonenzymatic antioxidant systems, malondialdehyde (MDA) content, and cell membrane permeability. Results showed that the combined treatment maintained higher storage quality of jujube than the other three treatments. The combined treatment significantly suppressed softening, weight, and color loss and maintained total soluble solid and titratable acidity. Moreover, the combined treatment improved antioxidant capacity showing high levels of nonenzymatic antioxidant systems (ascorbic acid and 1,1‐diphenyl‐2‐picrylhydrazyl radical scavenging capacity), and the activities of antioxidant enzymes (ascorbate peroxidase, superoxide dismutase, and catalase). These effects remarkably suppressed the increase of MDA level and cell membrane permeability, thereby inhibiting oxidative damage and alleviating quality deterioration. Furthermore, kinetic model indicated that AEW + MAP decreased rate constant of quality deterioration. Principal component analysis also showed that AEW + MAP treatment alleviated quality deterioration, with higher comprehensive score than other treatments. In summary, AEW + MAP was an effective preservation method to maintain storage quality and delay postharvest senescence of jujube. Practical Application: In this study, a combination of acidic electrolyzed water (AEW) with modified atmosphere packaging (MAP) has been found to have effective preservation effects on jujube. AEW + MAP treatment improved quality of jujube during cold storage mainly by enhancing antioxidant capacity. The combined treatment may provide a new preservation technology for jujube. [ABSTRACT FROM AUTHOR]

Near infrared hyperspectral imaging (NIR‐HSI) with a spectral range of 900 to 1700 nm was for the first time used to predict the changes of sugar content in Lingwu jujube during storage. Monte Carlo method was adopted to detect outliers, and multiple scattering correction (MSC), standard normal variate transformation (SNV), and Baseline were used to optimize modeling. Competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and interval random frog (IRF) were used to select optimal wavelengths. In addition, partial least square regression (PLSR) and support vector machine (SVM) modeling based on optimal wavelengths were compared. The results showed that 30, 30, and 24 wavelengths were selected by CARS; 106, 87, and 112 feature wavelengths were selected by iVISSA; and 96, 71, and 83 optimal wavelengths were selected by IRF for sucrose, fructose, and glucose, respectively. The CARS–PLSR models provided the best results for fructose and glucose, and iVISSA–SVM model was better for sucrose. The results indicated that NIR–HSI model may be used as a rapid and nondestructive method for the determination of sugar content in jujubes. [ABSTRACT FROM AUTHOR]

Jujube fruit may rapidly undergo maturity and senescence during storage, seriously affecting its commercialization. The study aimed to evaluate the role of acidic electrolyzed water (AEW) on energy and respiratory metabolism, and senescence in jujube fruit during cold storage. The results indicated that AEW improved ATP content and energy charge via increasing succinate dehydrogenase, cytochrome C oxidase, H+-ATPase, and Ca2+-ATPase activities and delayed jujube fruit senescence. The treatment was found to improve the activities of nicotinamide adenine dinucleotide kinase, 6-phosphogluconate dehydrogenase and glucose-6-phosphate dehydrogenase, and the content of NADPH, and reduce oxidative stress. Transcriptome analysis suggested that AEW regulated the expression of the genes associated with glycolysis, citrate cycle, pentose phosphate pathway, oxidative phosphorylation, ROS metabolism, transcription factors, phenylpropane biosynthesis, cell wall metabolism, and plant hormone signal transduction. Therefore, we concluded that AEW may delay the senescence of postharvest jujube fruit by regulating expression of the relevant genes and metabolic pathways. • AEW treatment promoted energy charge by activating energy metabolism-related enzymes. • AEW reduced cellular peroxidation by regulating pyridine nucleotide homeostasis. • AEW was involved in the regulation of differentially expressed genes associated with senescence. • AEW delayed senescence and maintained good quality of jujube fruit. [ABSTRACT FROM AUTHOR]

Ultraviolet (UV–C), a no residual environmentally friendly physical treatment, plays an important role in delaying the senescence in fruit. In this study, 'Lingwu long' jujubes were treated with UV-C (5 kJ m−2) to investigate the impacts of cell wall degrading enzymes (CWDEs) activities, reactive oxygen species (ROS) metabolism, and phenylpropanoid metabolism under storage at 4 ± 1 °C for 30 d. UV-C treatment reduced respiration rate and decay index. Treated fruit exhibited lower polygalacturonase (PG), pectinate lyases (PL), cellulase (Cel), and β-galactosidase (β-gal) activities which ultimately delayed the reduction of firmness. UV-C treatment increased hydrogen peroxide (H 2 O 2), free radical scavenging ability, and superoxide dismutase (SOD) and catalase (CAT) activities, reduced superoxide anion (O 2 −) and malondialdehyde (MDA) content. In addition, ascorbate peroxidase (APX), glutathione reductase (GR), dehydroascorbate reductase (DHAR), and monodehydroascorbate reductase (MDHAR) activities were activated by UV-C treatment, leading to glutathione (GSH) and ascorbic acid (AsA) increased. Besides, phenolic compounds of jujube fruit treated with UV-C were also increased, which might be due to the enhanced phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL) activities. In conclusion, UV-C was recommended for improving overall quality and alleviating senescence in jujube fruit. • UV-C delayed the softening rate in jujube fruit. • UV-C inhibited membrane lipid peroxidation in jujube fruit. • UV-C improved the reactive oxygen species scavenging ability in jujube fruit. • UV-C activated the phenylpropane metabolic pathway. • UV-C treatment showed great potential to delay senescence in jujube fruit. [ABSTRACT FROM AUTHOR]

• Make a quantitative damage device for Lingwu long jujube. • Spectral of samples was scanned by VIS/NIR hyperspectral system. • Using SPA, UVE, CARS, iVISSA and 2D-COS to select the characteristic wavelength. • Establish the PLS-DA classification models. Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube. [ABSTRACT FROM AUTHOR]

A hyperspectral imaging technique was used for acquiring reflectance images to identify common defects (bruise, insect-infestetation and cracks) on jujube fruit. Hyperspectral images of jujubes were evaluated from the regions of interest through principal component analysis (PCA) to select five optimal wavelengths (420,521,636,670,679 nm) from 300 samples in the spectral region of 400–1000 nm and four important wavelength (1028,1118,1359,1466 nm) in the region of 978–1586 nm. Compared with support vector machine (SVM) models, the soft independent modeling of class analogy (SIMCA) models of intact, cracked, bruised, and insect-infested jujubes based on five wavelengths in NIR showed good performance with high classification rates of 96%, 96%, 93.9% and 95.6%, respectively. This research demonstrates the feasibility of implementing hyperspectral imaging for identifying common defects and enhancing the product quality and marketability. [ABSTRACT FROM AUTHOR]

[Display omitted] • BOSS exhibited better superiority in wavelength selection compared with SCARS. • Texture features entropy, inertia, and gradient mean square error were highly correlated with the bruise over time (|r|>0.7). • Spectral data combined with textural features with the highest correlations improved prediction accuracy. • The 1D-CNN model had the advantage of handling large datasets. The timely detection and classification of bruised Lingwu long jujube is crucial for reducing economic losses in the agricultural industry. Traditional machine learning methods had been effectively used for bruise time classification with limited samples while the challenges were presented when the amount of data expanded. Deep learning methods can offer a solution. The hyperspectral image of the jujube sample was obtained using near-infrared hyperspectral imaging system (900–1700 nm). The optimal input variables were obtained by comparing the performance of the partial least squares-discriminant analysis, artificial neural network, and one-dimensional convolutional neural network (1D-CNN) models based on the spectral data and the fusion data of spectral and textural features. The optimal variable was the fusion data that could improve the model performance (from 92.68% to 96.10%). The above-mentioned three classifiers were used to establish models based on fusion data for two different dataset sizes consisting of large datasets (LD) (820) and small datasets (SD) (200) to explore the influence of dataset size on model performance. As the sample size increased, the 1D-CNN had good robustness with a classification rate of 94.00% for SD and 96.10% for LD in the prediction sets for all samples. The classification rate of prediction sets for 0 h after bruising increased from 90.00% to 92.68%, and from 80.00% to 90.24% in the class of 24 h after bruising. The findings provided a new approach to building a large spectral library and developing an online classification and detection system, thereby providing new ideas for the development of grading systems for Lingwu long jujube. [ABSTRACT FROM AUTHOR]

• The R spectra were transformed into two spectral units, A and K-M. • IVISSA algorithm was used to select characteristic wavelengths. • PLS-DA and SVM algorithms were used to identify sound and bruised jujubes. • A-Raw-iVISSA-PLS-DA was the optimal classification discriminant model. Bruising is one of the major challenges appearing in the postharvest grading and processing of Lingwu long jujubes, which leads to quality deterioration and microbial infection. In the present work, the spectra of reflectance (R), absorbance (A) and Kubelka-Munk (K-M) in Lingwu were obtained by hyperspectral imaging, and applied to non-destructively detect bruising symptoms. Spectra were preprocessed and characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and the interval variable iterative space shrinkage approach (iVISSA). Classification discriminant models were constructed by partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). By comparison, the results revealed that the A-raw-iVISSA-PLS-DA model showed the lowest errors in cross-validation, while the number of feature variables was the lowest accounting for 28.8 %, and the accuracies of the calibration and cross validation were 88.9 % and 100.0 %, respectively. In particular, this study demonstrated the feasibility to detect the early bruising degree in Lingwu long jujubes based on absorbance spectrum. Consequently, it also laid a foundation for future studies about detecting early bruising in small fruit with a rapid and non-destructive spectral-optical measurement. [ABSTRACT FROM AUTHOR]