Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy.

Kim, Sang-Yeon; Hong, Suk-Ju; Kim, Eungchan; Lee, Chang-Hyup; Kim, Ghiseok

doi:10.1016/j.jfoodeng.2022.111254

Cited by 27 publications

(11 citation statements)

References 37 publications

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“…Luo et al 41 for Brix prediction of ‘Navel’ orange built PLSR model using wavelength range 450–1000 nm and reported RMSE P and R P of 1.35 o Brix and 0.80, respectively. It is observed in both of the experiments 40 , 41 that the wavelength region includes visible region as well along with SWNIR region and the results are for a single cultivar dataset. McGlone et al 42 used NIR direct transmission measurement mode with a spectral window of 700–930 nm for Brix and TA prediction of ‘Satsuma’ mandarin.…”

Section: Introductionmentioning

confidence: 89%

“…The short-wave Vis–NIR option is preferred for commercial purposes due to (currently) lower hardware costs 7 , 8 . Kim et al 40 reported RMSE P and R P of 0.514 o Brix and 0.80 respectively for ‘Unshiu’ orange Brix prediction PLSR model trained using wavelength range 472-1156 nm. Luo et al 41 for Brix prediction of ‘Navel’ orange built PLSR model using wavelength range 450–1000 nm and reported RMSE P and R P of 1.35 o Brix and 0.80, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

Zeb

Qureshi

Ghafoor

et al. 2023

Sci Rep

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The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

Zeb

Qureshi

Ghafoor

et al. 2023

Sci Rep

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“…On the other hand, various methods are being developed to extract meaningful data from the Cataltas and Tutuncu / Selcuk J Agr Food Sci, (2022) 36 (3): 476-481 preprocessed spectrum. Application examples of artificial neural networks (Pérez-Marín et al 2007), deep learning (Cui & Fearn 2018), or ensemble methods (Kim et al 2023;Mishra et al 2020) have been encountered frequently in recent years. However, partial least square regression (PLSR), a cult method for qualitative analysis, is still the most popular method.…”

Section: Intrоduсtiоnmentioning

confidence: 99%

Determination of the ADF and IVOMD Content of Sugarcane Using Near Infrared Spectroscopy Coupled with Chemometrics

Cataltas

Tutuncu

2022

SJAFS

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Sugarcane is a plant whose quality parameters are required to be determined both for being one of the substances used in sugar production and for being used as animal feed. Near-infrared spectroscopy is a technique that has already been used for predicting the parameters of various plants and has gained popularity in recent years. This study proposes a near-infrared spectroscopy-based model for the rapid and effortless analysis of acid detergent fiber fraction and vitro organic matter digestibility parameters of the sugarcane plant. Partial least squares regression was combined with common preprocessing methods for modeling. This model yielded an R2CV value of 0.935 and 0.953 for the acid detergent fiber fraction and vitro organic matter digestibility parameters, respectively. Then, the spectra from three handheld spectrometers were combined using a proposed combination method to generate new spectra with higher spectral resolution. New models were built using these generated spectra and compared to the previous result. Obtained results showed that combining spectra from different spectrometers can improve model performance.

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“…Stacked models within ensemble learning follow the paradigm of ‘Wisdom of the Crowd’, where predictions from base models are concatenated and fed into a meta‐model for training, aiming to achieve enhanced predictive accuracy 12 . Ensemble learning models have found application in analyzing the composition of different food ingredients, including determining the total nitrogen content in winter wheat, 13 the total flavonoid content in berries, 14 and the sugar content in citrus fruits 15 . These investigations reported satisfactory predictive outcomes, serving as valuable precedents for creating an integrated learning model aimed at predicting both saccharification power and protein content in wheat.…”

Section: Introductionmentioning

confidence: 99%

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

Huang,

Tian,

Yang

et al. 2024

J Sci Food Agric

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BACKGROUNDWheat is one of the key ingredients used to make Chinese liquor, and its saccharification power and protein content directly affect the quality of the liquor. In pursuit of a non‐destructive assessment of wheat components and the optimization of raw material proportions in liquor, this study introduces a precise predictive model that integrates hyperspectral imaging (HSI) with stacked ensemble learning (SEL).RESULTSThis study extracted hyperspectral information from 14 different varieties of wheat and employed various algorithms for preprocessing. It was observed that multiplicative scatter correction (MSC) emerged as the most effective spectral preprocessing method. The feature wavelengths were extracted from the preprocessed spectral data using three different feature extraction methods. Then, single models (support vector machine (SVM), backpropagation neural network (BPNN), random forest (RF), and gradient boosting tree (XGBoost)) and a SEL model were developed to compare the prediction accuracies of the SEL model and the single models based on the full‐band spectral data and the characteristic wavelengths. The findings indicate that the MSC–competitive adaptive reweighted sampling–SEL model demonstrated the highest prediction accuracy, with Rp2 (test set‐determined coefficient) values of 0.9308 and 0.9939 for predicting the saccharification power and protein content and root mean square error of the test set values of 0.0081 U and 0.0116 g kg−1, respectively.CONCLUSIONThe predictive model established in this study, integrating HSI and SEL models, accurately detected wheat saccharification power and protein content. This validation underscores the practical potential of the SEL model and holds significant importance for non‐destructive component analysis of raw materials used in liquor. © 2024 Society of Chemical Industry.

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Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy.

Cited by 27 publications

References 37 publications

Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

Determination of the ADF and IVOMD Content of Sugarcane Using Near Infrared Spectroscopy Coupled with Chemometrics

Detection of wheat saccharification power and protein content using stacked models integrated with hyperspectral imaging

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