Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS

Wu, Xiugang; Zeng, Shupeng; Fu, Hailing; Wu, Bin; Zhou, Haoxiang; Dai, Chunxia

doi:10.1016/j.fochx.2023.100666

Cited by 5 publications

(7 citation statements)

References 28 publications

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“…In other words, the model was looking in the correct region(s) of the NIR spectrum to be able to detect protein. This was comparable toWu et al (2023), who used this wavelength region in the NIRS prediction of protein content of corn.…”

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confidence: 75%

Assessment of bioactive compounds in faba bean using infrared spectroscopy

Johnson,

Walsh,

Naiker

2023

Legume Science

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Faba bean (Vicia faba) is growing in popularity in Australia, partly due to its higher levels of health‐benefiting compounds compared to other grain crops. This study investigated infrared spectroscopy for predicting levels of bioactive compounds such as antioxidants and phenolics in faba bean flour. Calibration models were performed on 60 samples of faba bean, comprising 10 varieties grown across two field locations in 1 year. For model validation, an independent test set comprising the same varieties grown in a different year was utilised. Near‐infrared spectroscopy (NIRS) showed promise for the prediction of total phenolic content, with an R2pred of 0.66 and root mean square error of prediction (RMSEP) of 76 mg/100 g. Similarly, prediction of ferric reducing antioxidant power, a measure of antioxidant activity, gave an R2pred of 0.59 and RMSEP of 87 mg/100 g. Additionally, moving window optimisation was used to determine the most important wavelength region for the prediction of these analytes. Fourier transform infrared spectroscopy did not yield any suitable models for the analytes investigated. Although the NIRS models developed were not capable of exactly quantifying phenolic or antioxidant content, infrared spectroscopy appears useful for rapidly discriminating between samples containing high and low levels of phenolics or antioxidant compounds. With further refinement, this technique could potentially be applied for the quality assurance of phenolic content or antioxidant capacity in faba bean seeds.

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supporting

confidence: 75%

Assessment of bioactive compounds in faba bean using infrared spectroscopy

Johnson,

Walsh,

Naiker

2023

Legume Science

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“…The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32,16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models.…”

Section: Resultsmentioning

confidence: 99%

“…It plays a crucial role in machine learning, but their impact on deep learning is unclear. To explore the feasibility of using four algorithms to establish simplified models for bacterial contamination status and species discrimination, the common characteristic wavelength extraction methods genetic algorithm (GA), competitive adaptive reweighting algorithm (CARS) and successive projections algorithm (SPA) (32,33) were used to extract the characteristic wavelengths closely related to the bacterial contamination status and the species of pathogenic bacteria, and the simplified models were established based on the characteristic wavelengths.…”

Section: Characteristic Wavelengths Extractionmentioning

confidence: 99%

“…The results showed that the 1D-CNN model was very suitable for establishing the discrimination model of pathogenic bacteria in mutton. According to the fact that the smaller the convolution sum, the smaller the computational cost of the model, the 1D-CNN with convolution sum of (32,16) was selected to establish the pathogen species discrimination model. When the convolution kernel of the 1D-CNN model was set to (32,16), the effects of three activation functions (sigmoid, tanh and relu) on the pathogen species dis-crimination model were investigated.…”

Section: Optimization and Determination Of Hyperparameters Of 1d-cnn ...mentioning

confidence: 99%

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Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI

Bai,

Du,

Zhu

et al. 2024

Front. Nutr.

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IntroductionRapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers.ObjectivesThe feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored.Materials and methodsThe hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored.Results and discussionThe best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments.ConclusionThe results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

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“…CARS is a variable selection method combining Monte Carlo sampling and PLS, whose variable selection strategy is derived from Darwin's principle of 'survival of the fittest', and uses adaptive reweighting sampling (ARF) and exponential descent function (EDF) as the indices for wavelength selection. 25 The number of Monte Carlo samples in this investigation was fixed at 50, and tenfold cross-validation was employed. SPA can use projection analysis of vectors to map high-dimensional data onto a onedimensional or low-dimensional space and select the ensemble of spectral feature variables with minimum collinearity and redundancy.…”

Section: Characteristic Wavelength Selectionmentioning

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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Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS

Cited by 5 publications

References 28 publications

Assessment of bioactive compounds in faba bean using infrared spectroscopy

Assessment of bioactive compounds in faba bean using infrared spectroscopy

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI

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

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