Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging

Gao, Qiang; Wang, Peng; Niu, Tong; He, Dongjian; Wang, Meili; Yang, Huijun; Zhao, Xiaoqiang

doi:10.1016/j.foodchem.2021.131013

Cited by 32 publications

(13 citation statements)

References 36 publications

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“…The prediction accuracy of the G and I models was also good, of which the R 2 , MAE, and RMSE of the G model reached 0.63, 0.1, and 0.13, respectively, which were better than those of the I model; however, the consistency index ( d ) of the G model was 0.7, which was slightly lower than the 0.73 of the I model. The GPI evaluation index was used to comprehensively evaluate the indicators of each prediction model [ 13 ]. The three models with greatest evaluation scores were the R , G , and I models, and their comprehensive evaluation scores were 0.87, 0.39, and 0.14, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, with the support of imaging technology and machine vision technology, crop maturity prediction has gradually become a popular research topic [ 8 – 10 ]. Previous studies have used image technology to predict crop maturity, mainly via spectral information prediction [ 11 ], electronic noses, and electronic tongues [ 12 ] combined with partial least squares regression analysis and via color eigenvalues combined with back-propagation neural networks [ 13 ]. The BP neural network is considered a highly accurate model when the internal mechanism and relation are uncertain [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Wei

et al. 2022

Plant Phenomics

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To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R,G,B,H,S,and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc<0.7) and a mature stage (after which color changes occurred) (0.7≤Mc<1). When predicting grape maturity based on the R,G,B,H,I,and S color values, the R,G,and I as well as G,H,and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc’s predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Wei

et al. 2022

Plant Phenomics

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“…A certain amount of noise and interference information was generated due to external factors during the data acquisition [ 40 ]. Hence, preprocessing the original spectra is necessary.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Fan

Zhu

et al. 2022

Foods

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The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

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“…To test this further, we evaluated the preservative effect of CMs on peaches by detecting freshness parameters including SSC and TA. The SSC of peach fruit was measured by a refractometer (Rudolph, New York, USA) as described by Gao et al 32 Peach fruit (20 g) was mashed for 5 min at speed of 5000 rpm. The homogenate was filtered by gauze, and two drops were put on the central prism of the refractometer.…”

Section: Methodsmentioning

confidence: 99%

Citral Essential Oil-Loaded Microcapsules by Simple Coacervation and Its Application on Peach Preservation

Zheng

et al. 2022

ACS Omega

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Citral essential oil (CEO) was encapsulated by the single coalescence method, and its stability, release properties, and ability to maintain freshness were evaluated for the first time. The microshape characteristics of a CEO-loaded microcapsule (CM) were analyzed by inverted microscopy (OM) and scanning electron microscopy (SEM). The encapsulation efficiency, stability, and release behavior of CEO were evaluated using Fourier transform infrared spectroscopy (FTIR), thermogravimetric/differential thermal comprehensive analysis (TG/DSC), and gas chromatography mass spectrometry (GC/MS). Moreover, peaches were used to evaluate the preservation properties of the CEO-loaded microcapsule. The results showed that the microcapsule produced using simple coacervation had better microstructure and the ability to reduce and control the release of citral essential oil. The qualities of peaches, such as appearance changes, hardness, soluble solid content, total acids, and total bacterial counts, were significantly improved in the CM system during storage, in comparison with the control and cold storage groups. Therefore, the CM has potential applications and development prospects in the food, drug, and other industries.

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Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging

Cited by 32 publications

References 36 publications

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Citral Essential Oil-Loaded Microcapsules by Simple Coacervation and Its Application on Peach Preservation

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