Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm

Xu, Min; Sun, Jun; Cheng, Jiehong; Yao, Kunshan; Wu, Xiugang; Zhou, Xin

doi:10.1111/ijfs.16173

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…In 2023, Min Xu et al used hyperspectral technology with the deep-learning-based stacked autoencoder (SAE) method to construct a deep learning model to quickly detect the TSS in Kyoho grapes. As such, nondestructive testing (NDT) [ 18 ] represents a suite of analytical techniques employed for the evaluation of a material’s properties without causing damage.…”

Section: Introductionmentioning

confidence: 99%

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears

Ouyang,

Tang,

et al. 2024

Plants

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Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG–GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG–SVR model. The SG–CARS–GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG–CARS–GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.

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Section: Introductionmentioning

confidence: 99%

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears

Ouyang,

Tang,

et al. 2024

Plants

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“…Machine learning, a subset of artificial intelligence, enables computers to learn patterns and make predictions through data analysis. In food science, it finds applications in areas such as food quality prediction, flavour prediction, sensory analysis, food safety, recipe generation, shelf‐life forecasting, food pairing, and in vitro digestion (Yavuzer & Köse, 2022; Huang et al ., 2023; Xu et al ., 2023; Yavuzer, 2023). Artificial neural networks (ANNs), a type of machine learning algorithm, play a significant role in enhancing food production quality, safety, and efficiency (Zhang et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Assessing nutritional probing and storage stability of functional Aloe vera (Aloe barbadensis) based guava jam: a machine learning approach for predictive modelling

Ibraheem,

Tariq,

Ali

et al. 2024

Int J of Food Sci Tech

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SummaryThis research aimed to explore the influence of nutritional adjustments and storage stability on a functional, reduced‐calorie guava jam incorporating Aloe vera. Over two months, comprehensive analysis assessed physicochemical properties, sensory traits, microbial stability, and shelf‐life. The addition of Aloe vera gel resulted in significant improvements in pH (3.00 to 3.65), total soluble solids (40.10 to 42.20°Brix), antioxidant activity (36.85% to 81.09%), moisture content (29.48% to 38.82%), water activity (0.78 to 0.84), ash content (0.29% to 0.45%), fat content (0.14% to 0.19%), fibre content (1.05% to 1.86%), and the colour values. Moreover, b* scores for colour indication improved from 15.09 to 18.86. Texture attributes of cohesiveness and firmness improved significantly. Sensory evaluation favoured the T2 variant (20% Aloe vera gel), suggesting it as the optimal formulation. Furthermore, artificial neural networks (ANNs), a technique of machine learning, were utilised to predict guava jam behaviour, with 99% accuracy. The study discovered substantial changes in pH, total soluble solids, antioxidant activity, moisture content, and textural qualities, indicating that Aloe vera supplementation could improve guava jam quality and shelf‐life. The results of ANN predictions about antioxidants and cohesiveness provide information about the product's performance during storage.

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Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

Ropelewska,

Noutfia

2023

Eur Food Res Technol

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Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods ( – 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions.

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Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm

Cited by 8 publications

References 31 publications

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears

Assessing nutritional probing and storage stability of functional Aloe vera (Aloe barbadensis) based guava jam: a machine learning approach for predictive modelling

Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

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