Nondestructive monitoring storage quality of apples at different temperatures by near‐infrared transmittance spectroscopy

Guo, Zhiming; Wang, Mingming; Ali, Shujat; Wu, Jingzhu; El‐Seedi, Hesham R.; Shi, Jiyong; Ouyang, Qin; Chen, Quansheng; Zhang, Xiaobo

doi:10.1002/fsn3.1669

Cited by 23 publications

(7 citation statements)

References 35 publications

(39 reference statements)

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“…also showed that CARS extracted fewer feature bands than UVE-SPA, but also lost more useful bands, resulting in a worse subsequent regression effect. This was consistent with the findings of Guo et al ( Guo et al., 2020 ). They compared the results of the UVE-SPA-PLS and CARS-SPA-PLS models with those of the UVE-PLS and CARS-PLS models, and found that the predictive performance of the SPA engagement model was slightly lower than that of UVE-PLS and CARS-PLS.…”

Section: Resultssupporting

confidence: 94%

Low temperature response index for monitoring freezing injury of tea plant

Mao

He²,

Wang³

et al. 2023

Front. Plant Sci.

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Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R2 = 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants.

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Section: Resultssupporting

confidence: 94%

Low temperature response index for monitoring freezing injury of tea plant

Mao

He²,

Wang³

et al. 2023

Front. Plant Sci.

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“…Noteworthily, attention is given to increasing the universality of the models taking into account varying conditions of the measurement. For instance, Guo et al recently compared the effects of temperature on the models and used preprocessing methods and variable selection methods to improve the prediction performance under varying conditions [334]. The results showed that the NIR system could accurately predict the quality attributes of apples stored for different periods, with promising results for firmness, SSC, titratable acidity, and vitamin C. Noteworthily, attention is given to increasing the universality of the models taking into account varying conditions of the measurement.…”

Section: Optimization Of Storage Conditionsmentioning

confidence: 99%

“…The results showed that the NIR system could accurately predict the quality attributes of apples stored for different periods, with promising results for firmness, SSC, titratable acidity, and vitamin C. Noteworthily, attention is given to increasing the universality of the models taking into account varying conditions of the measurement. For instance, Guo et al recently compared the effects of temperature on the models and used preprocessing methods and variable selection methods to improve the prediction performance under varying conditions [334]. The results showed that the NIR system could accurately predict the quality attributes of apples stored for different periods, with promising results for firmness, SSC, titratable acidity, and vitamin C.…”

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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“…Near infrared (NIR) spectroscopy has gained remarkable value as a non-destructive analytical technique and it has become the tool of choice in several fields of application [1]. Because of the advantages of the capacity for quickmeas [2], nondestructive [3] and capable of on-site analysis [4,5], the portable spectrometers are extensively used determining and quantitative analysis of chemical components of various samples in the outdoor environment [6][7][8][9]. However, compared with laboratory conditions, the outdoor working environment of the portable instruments is more complicated [10].…”

Section: Introductionmentioning

confidence: 99%

A measurement method in near infrared spectroscopy for reference correction with the homologous optical beams

et al. 2021

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In order to improve the stability of the portable near infrared (NIR) spectrometer, a measurement method in NIR spectroscopy (e.g. reference correction with the homologous optical beam) was proposed. Based on the measurement method, the contrast sampling mechanism was designed to obtain the reference beam from light source to ensure that the reference beam and the light source drift in the same trend. The reference beam can be used to eliminate the drift of the measured spectral data due to the drift of the light source. Then, by using the contrast sampling mechanism, the NIR spectrometer for reference correction with the homologous optical beams was developed. Using the newly designed spectrometer, the experiment to compare the performance of the single optical path detection method and dual optical path detection method was conducted. According to experimental results, the stability of the newly designed spectrometer achieved substantial improvement. The baseline drift of the reflectance spectra of gilding aluminium plate was decreased from 0.37% (with single optical path detection) to 0.074%, while the baseline drift of the reflectance spectra of kaolin was decreased from 2.92% to 0.16%.

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Nondestructive monitoring storage quality of apples at different temperatures by near‐infrared transmittance spectroscopy

Cited by 23 publications

References 35 publications

Low temperature response index for monitoring freezing injury of tea plant

Low temperature response index for monitoring freezing injury of tea plant

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

A measurement method in near infrared spectroscopy for reference correction with the homologous optical beams

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