Establishment of a multi-position general model for evaluation of watercore and soluble solid content in ‘Fuji’ apples using on-line full-transmittance visible and near infrared spectroscopy

Li, Jiangbo; Zhang, Yifei; Zhang, Qian; Duan, Dandan; Chen, Liping

doi:10.1016/j.jfca.2023.105150

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…These changes in the chemical and physical properties of apples during storage, such as loss of firmness, changes in sugar and acid content, and development of off-flavors and discoloration, can be monitored with these techniques. Particularly useful is the ability to detect internal defects, such as watercore and decay, which can develop during storage but may not be visible on the surface of the fruit and are challenging to detect online during production [318][319][320][321][322].…”

Section: Optimization Of Storage Conditionsmentioning

confidence: 99%

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Grabska

Beć

Ueno

et al. 2023

Foods

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Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world’s most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

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Section: Optimization Of Storage Conditionsmentioning

confidence: 99%

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Grabska

Beć

Ueno

et al. 2023

Foods

View full text Add to dashboard Cite

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“…Compared with other studies on apple SSC online detection, the best prediction model constructed in this study is better than Li et al (2023) [42], Xia et al (2019) [16], and Tian et al (2019) [43], and slightly lower than Chang et al (2023) [44] and Zheng et al (2023) [45]. Moreover, the spectrometer used in this study has a lower cost.…”

Section: Discussionmentioning

confidence: 55%

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

Li,

Peng,

et al. 2024

Foods

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Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

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“…Figure 1 demonstrate RMSECV as a function of the number of PLS components for the selected intervals. The optimal subinterval selected for the final run was [1,7,12], and the minimum RMSECV value was 0.3981, resulting in a total of 197 spectral variables, accounting for 21.4% of the total variables. Next, the preferred subintervals were downscaled using the characteristic wavelength extraction algorithm to reduce the dimensionality of the data further.…”

Section: Sipls-based Feature Interval Selectionmentioning

confidence: 99%

“…Previously, researchers used various spectroscopic techniques to perform quantitative analysis. For instance, Wu et al [12] employed near-infrared and Raman spectroscopy to develop a quantitative model for assessing mung bean moisture, protein, and total starch content. Teye et al [13] used near-infrared spectroscopy for the non-destructive determination of total fat content in cocoa bean samples.…”

Section: Introductionmentioning

confidence: 99%

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

Han,

Guo,

Zhang

et al. 2023

Agriculture

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Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, Rc and Rp, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an Rc value of 0.9841 and an Rp value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton.

show abstract

Establishment of a multi-position general model for evaluation of watercore and soluble solid content in ‘Fuji’ apples using on-line full-transmittance visible and near infrared spectroscopy

Cited by 15 publications

References 39 publications

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

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