Genetic algorithm optimized non-destructive prediction on property of mechanically injured peaches during postharvest storage by portable visible/shortwave near-infrared spectroscopy

Du, Xingfan; Li, Xiaoyü; Liu, Ying; Zhou, Wei; Li, Jianlong

doi:10.1016/j.scienta.2019.01.057

Cited by 22 publications

(18 citation statements)

References 30 publications

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“…Defects in fruits with lower chlorophyll content may not show significant chlorophyll band variation, as is the case with internal browning in apples (60). Evaluating specific chemical bonds and determining the wave points is significantly easier than characterizing TSS or SSC, each of which contains several compounds (80) and is thus tedious to identify on a spectral reflectance plot. The wavelength range of SSC for cherries was 900-2,500 nm, a large waveband that needs further analysis (79).…”

Section: Accuracy Findings Referencesmentioning

confidence: 99%

“…The classification of defect detection is usually done subjectively, i.e., not quantitatively, meaning that human error can occur ( 10 ). When the spectra are used for chemometric or qualitative measurement, they should be immediately assessed after the spectral measurement to reduce errors caused by the increase of time ( 80 ).…”

Section: Applications Of Spectroscopic Techniquesmentioning

confidence: 99%

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Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

et al. 2022

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The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

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Section: Accuracy Findings Referencesmentioning

confidence: 99%

Section: Applications Of Spectroscopic Techniquesmentioning

confidence: 99%

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

et al. 2022

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“…It automatically obtains and guides the optimized search space through the randomized search method that simulates the random search and the optimization solution method Guo, Wang, et al, 2021). In the data analysis of gas sensor, GA can quickly extract feature variables and eliminate the interference of irrelevant information (Du, Li, Liu, Zhou, & Li, 2019). It has the characteristics of simple principle and operation, strong universality, and can reach the global optimal in a short time.…”

Section: Genetic Algorithm-partial Least Squarementioning

confidence: 99%

Identification of the apple spoilage causative fungi and prediction of the spoilage degree using electronic nose

Guo

Sun

et al. 2021

J Food Process Engineering

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Apple is resistant to storage, but it is susceptible to fungal infection during transportation and storage, resulting in serious losses after harvest. A convenient and nondestructive monitoring method for fungi-inoculated apples was proposed in this research. Four dominant spoilage fungi, including Aspergillus niger, Penicillium expansum, Penicillium chrysogenum, and Alternaria alternata, were inoculated on apple samples. The volatile information of samples with different degrees of spoilage was obtained by gas sensors. The pattern recognition methods were compared to classify the fungi and degrees of spoilage. Back propagation-artificial neural networks (BP-ANN) had the best identification model result with the highest recognition rates of 95.62 and 99.58% for fungi and spoilage degrees, respectively. The variable selection methods were employed, and variables of the gas sensors data for the prediction of apple spoilage area were optimized. The best prediction models of Aspergillus niger, Penicillium expansum, Penicillium chrysogenum, and Alternaria alternata were 0.854, 0.939, 0.909, and 0.918, respectively. The results show that the gas sensors can be used as a nondestructive technique in apple fungi infection evaluation. This proposed fruit spoilage detection technology is expected to provide a reference for the early detection of apple spoilage to promote food quality and safety inspection. Practical ApplicationsThis research used gas sensors to identify the four main spoilage fungi of apples and predicted the spoilage degree of apples using established prediction models. The apple spoilage detection method adopted in this research provides a reference for the early detection of fruit spoilage, which is helpful for apple storage and reduces the economic loss caused by corruption. It is an important measure to help ensure the economic benefits of apple and provide consumers with a large number of highquality apple products.

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“…Generally, the selected variables are concentrated in several intervals, and the variables need to be further simplified. Some typical individual selection methods include Monte Carlo non‐information variable elimination (MCUVE) (Li et al, 2014; Yan et al, 2019), competitive adaptive reweighted sampling (CARS) (Bai et al, 2019; Jiang et al, 2015; Yan et al, 2019), genetic algorithm (GA) (Du et al, 2019), successive projections algorithm (SPA) (Li et al, 2014; Liu et al, 2017) and bootstrapping soft shrinkage (BOSS) (Deng et al, 2016). The MCUVE selects the useful variables according to the stability of variables.…”

Section: Introductionmentioning

confidence: 99%

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

Liu

et al. 2021

Food and Energy Security

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Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lignin content of pears is therefore required. An improved variables selection method called ‘the bootstrapping soft shrinkage combined with frequency and regression coefficient of variables (FRCBOSS)’ was therefore developed based on ‘the bootstrapping soft shrinkage (BOSS)’technique, to identify the characteristic wavelengths of near‐infrared (NIR) spectra for non‐destructive and rapid analysis of lignin. Sub‐models were generated by weighted bootstrap sampling (WBS) in the FRCBOSS method. For the BOSS method, the new weights of variables were determined only as the absolute values of regression coefficients of variables in each iteration. In contrast, the FRCBOSS method also considers the frequency of variables in variable space. Moreover, the FRCBOSS algorithm overcomes the disadvantage of BOSS in selecting variables, which could incorporate useful wavelengths that would otherwise be removed by the BOSS method. In addition, a range of different pre‐treatment methods were used for comparison in the detection of lignin in the Snow pears. These include Savitzky–Golay Smoothing (SG), Normalization (NORM), Standard Normal Variate (SNV), and 1st Derivative (D1), as well as a combination of these methods and the different variables selection method (SiPLS, SiPLS‐SPA, SiPLS‐CARS, SiPLS‐BOSS, and SiPLS‐FRCBOSS). The number of variables selected by FRCBOSS was a little larger than that selected by BOSS. The partial least square regression (PLSR) model based on the 19 variables selected by SiPLS‐FRCBOSS method had the best prediction ability, with a Rp value of prediction of 0.880 and a RMSEP value of 1.004%. We conclude that NIR diffuse reflectance spectroscopy technology combined with FRC‐BOSS is an accurate and useful tool for the non‐destructive and rapid determination of pear lignin contents.

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Genetic algorithm optimized non-destructive prediction on property of mechanically injured peaches during postharvest storage by portable visible/shortwave near-infrared spectroscopy

Cited by 22 publications

References 30 publications

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

Identification of the apple spoilage causative fungi and prediction of the spoilage degree using electronic nose

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

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