Study of Malus Asiatica Nakai’s firmness during different shelf lives based on visible/near-infrared spectroscopy

Xue, Jianxin; Zhang, Shujuan; Sun, Haixia; Zhou, Ji

doi:10.1016/j.mcm.2012.12.021

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…The strong absorption band at 6896 cm −1 is connected with the first overtone of O-H deformation. The significant absorption of water molecules in the near-infrared region is related to the fluctuations in the curves corresponding to the development of these bands [ 21 ]. Wave numbers of roughly 8403, 6896, and 5154 cm −1 were screened in all of them, showing the rationality of the wave band screening.…”

Section: Resultsmentioning

confidence: 99%

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

Wang,

Fu,

et al. 2024

Foods

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The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits’ spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000–4000 cm−1. The hardness of passion fruit’s outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

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Section: Resultsmentioning

confidence: 99%

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

Wang,

Fu,

et al. 2024

Foods

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“…Meanwhile, the wavelength of 912 nm is closely related to the absorption of water and starch. The results also showed that the water property caused by the O–H bonds and starch can be detected at 970 nm [ 41 , 42 , 43 ] The recorded spectrum contributes to the presence of organic substances, as inferred from the spectral bands that arise from the interaction between molecular bonds such as O–H, C–H, C–O, and N–H with the incoming radiation. The vibrational energy changes visible in these interactions encompass two distinct vibration patterns: stretch vibration and bend vibration.…”

Section: Resultsmentioning

confidence: 99%

Comparison of color spectrophotometer and Vis/NIR spectroscopy on assessing natural pigments of cucumber applied with different ethephon concentrations

Kusumiyati,

Putri

2023

Heliyon

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“…In this study, the linear method with variable selection by competitive adaptive reweighted sampling (CARS) algorithm showed better results than SVM model. Another work where PLS showed better results than SVM in determining firmness was conducted by Xue et al [ 195 ] to analyze Chinese pear-leaved crabapple.…”

Section: Application Of Non-linear Methods On Food Groupsmentioning

confidence: 99%

“…However, linear models can achieve high accuracy when used with a small number of samples. Examples are highlighted in the papers that discuss PLS [195,197,214] and MLR [230] models.…”

Section: Summary From Classification and Predictionmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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Study of Malus Asiatica Nakai’s firmness during different shelf lives based on visible/near-infrared spectroscopy

Cited by 13 publications

References 10 publications

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

Comparison of color spectrophotometer and Vis/NIR spectroscopy on assessing natural pigments of cucumber applied with different ethephon concentrations

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

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