Quality monitoring method for apples of different maturity under long-term cold storage

Zhang, Bo; Zhang, Mengsheng; Shen, Maosheng; Li, Hao; Zhang, Zhongxiong; Zhang, Haihui; Zhao-yong, Zhou; Ren, Xiaolin; Ding, Yuduan; Xing, Libo; Zhao, Juan

doi:10.1016/j.infrared.2020.103580

Cited by 38 publications

(21 citation statements)

References 51 publications

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“…Decreases in the starch level in ripening fruit may be the result of up-regulated expression of key genes related to starch synthesis (i.e., SS and SBE genes) and metabolism (i.e., β-AMY, ISA, and BMY genes) (Figure 6). Recent studies revealed that the fruit starch content is negatively correlated with fruit maturity (Zhang et al, 2020(Zhang et al, , 2021.…”

Section: Discussionmentioning

confidence: 99%

“…Apple fruit texture indices (i.e., pericarp hardness and resilience, fresh fruit hardness and resilience, and fresh fruit fiber index) were measured using the texture analyzer (Universal TA: Stable Micro Systems Ltd.) with a P/2 measuring probe. Analyses were completed at a puncture depth of 10 mm and a puncture speed of 1 mm/s as previously described (Zhang et al, 2021). Additionally, the SSC was measured using the PAL-BX/ ACID5 portable digital refractometer designed for apple juice (ATAGO Co. Ltd.), whereas the soluble organic acid content was measured using the JC10-GMK-855 apple acidity meter (G-won Hitech Co., Ltd).…”

Section: Measurement Of Fruit Quality Indicesmentioning

confidence: 99%

“…Apple fruit maturity was determined based on starch staining, during which the cut surface was stained with iodine solution as previously described (Doerflinger et al, 2015). The stained starch area was calculated by processing images using a K-means clustering algorithm (K = 3) to segment the original image and determine maturity (Zhang et al, 2021).…”

Section: Starch Staining To Determine Maturitymentioning

confidence: 99%

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Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity

Wang,

et al. 2024

Physiologia Plantarum

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Harvest maturity significantly affects the quality of apple fruit in post‐harvest storage process. Although the regulatory mechanisms underlying fruit ripening have been studied, the associated epigenetic modifications remain unclear. Thus, we compared the DNA methylation changes and the transcriptional responses of mature fruit (MF) and immature fruit (NF). There were significant correlations between DNA methylation and gene expression. Moreover, the sugar contents (sucrose, glucose, and fructose) were higher in MF than in NF, whereas the opposite pattern was detected for the starch content. The expression‐level differences were due to DNA methylations and ultimately resulted in diverse fruit textures and ripeness. Furthermore, the higher ethylene, auxin, and abscisic acid levels in MF than in NF, which influenced the fruit texture and ripening, were associated with multiple differentially expressed genes in hormone synthesis, signaling, and response pathways (ACS, ACO, ZEP, NCED, and ABA2) that were regulated by DNA methylations. Multiple transcription factor genes involved in regulating fruit ripening and quality via changes in DNA methylation were identified, including MIKCC‐type MADS‐box genes and fruit ripening‐related genes (NAP, SPL, WRKY, and NAC genes). These findings reflect the diversity in the epigenetic regulation of gene expression and may be relevant for elucidating the epigenetic regulatory mechanism underlying the ripening and quality of apple fruit with differing harvest maturity.

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Section: Discussionmentioning

confidence: 99%

Section: Measurement Of Fruit Quality Indicesmentioning

confidence: 99%

Section: Starch Staining To Determine Maturitymentioning

confidence: 99%

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Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity

Wang,

et al. 2024

Physiologia Plantarum

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“…At present, near-infrared spectroscopy is applied to the detection and research of fruit quality, for example, the internal dry matter content (DMC) and soluble solid concentration (SSC) of peach fruit, and the prediction of the titratable acidity, pH value and maturity index in citrus [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Many investigators have also used near-infrared spectroscopy to predict the mineral content of different trees or crops.…”

Section: Introductionmentioning

confidence: 99%

Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of ‘Huangguan’ Pears Established by Using Near-Infrared Spectroscopy

Fang

Jin

Lin

et al. 2022

Foods

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It has been proved that the imbalance of the proportion of elements of ‘Huangguan’ pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears’ appearance quality and economic benefits. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of ‘Huangguan’ pears nondestructively and conveniently by using near-infrared spectroscopy (900–1700 nm) technology. Firstly, 12 algorithms were used to preprocess the original spectral data. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of ‘Huangguan’ pears accurately and quickly. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of ‘Huangguan’ pears caused by a lack of nutrients.

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“…Relatively speaking, the visible and near-infrared (Vis/NIR) spectroscopy is a non-destructive, rapid, real-time, and low-cost technology [ 18 , 19 ] for internal quality detection of agricultural products [ 20 ]. It has been used for evaluation of fruit quality attributes such as bruise [ 21 , 22 ], defects [ 23 , 24 ], firmness [ 25 , 26 ], SSC [ 27 , 28 , 29 ], brown [ 27 , 30 ], and moldy core [ 17 , 31 ]. The transmission and diffuse reflection modes are two common ways to collect spectra of tested samples.…”

Section: Introductionmentioning

confidence: 99%

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Zhang

Cai

Fan

et al. 2021

Foods

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Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

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Quality monitoring method for apples of different maturity under long-term cold storage

Cited by 38 publications

References 51 publications

Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity

Epigenomic mechanism regulating the quality and ripeness of apple fruit with differing harvest maturity

Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of ‘Huangguan’ Pears Established by Using Near-Infrared Spectroscopy

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

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