Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning

Liu, Cong; Yang, Simon X.; Deng, Lie

doi:10.1016/j.jfoodeng.2015.03.022

Cited by 45 publications

(21 citation statements)

References 24 publications

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Section: Introductionmentioning

confidence: 99%

“…Machine learning is a research field related to the automatic pattern recognition from a given knowledge database towards making decisions and predictions. 21 Some recent researches in the agricultural field have used NIR and ML tools with good prediction results becoming a new alternative for the traditional form of validation. 22 Therefore, the present work aims at contributing in the development of a rapid and nondestructive approach to classify different types of chicken parts, which can be potentially used in chicken products adulteration, through the use of NIR technique performed by a portable spectrophotometer associated with ML.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

et al. 2018

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Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical-chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900-1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

et al. 2018

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“…With the optimization in the previous section, we summarize the iterative algorithm to optimize the problem in (13). The iterative algorithm is given in Algorithm 1.…”

Section: Iterative Algorithmmentioning

confidence: 99%

“…Machine learning aims to train a predictive model from a training set of inputout pairs, and then use the model to predict an unknown output from a given test input [14,41,38,13,34,1,18]. In this paper, we focus on the machine learning problem of binary pattern classification.…”

Section: Introductionmentioning

confidence: 99%

Sparse learning of maximum likelihood model for optimization of complex loss function

Zhang

Chandrasekar

2015

Neural Comput & Applic

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Traditional machine learning methods usually minimize a simple loss function to learn a predictive model and then use a complex performance measure to measure the prediction performance. However, minimizing a simple loss function cannot guarantee an optimal performance. In this paper, we study the problem of optimizing the complex performance measure directly to obtain a predictive model. We proposed to construct a maximum likelihood model for this problem, and to learn the model parameter, we minimize a complex loss function corresponding to the desired complex performance measure. To optimize the loss function, we approximate the upper bound of the complex loss. We also propose to impose the sparsity to the model parameter to obtain a sparse model. An objective was constructed by combining the upper bound of the loss function and the sparsity of the model parameter, and we develop an iterative algorithm to minimize it by using the fast iterative shrinkage-thresholding algorithm framework. The experiments on optimization on three different complex performance measures, including F-score, receiver operating characteristic curve, and recall precision curve break-even point, over three real-world applications, aircraft event recognition of civil aviation safety, intrusion detection in wireless mesh networks, and image classification, show the advantages of the proposed method over state-of-the-art methods.

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“…Many classification and machine learning methods have been used for measurement data analyses. Recent studies include machine learning with optics, NIR and X-rays [27][28][29][30][31][32]. Electromagnetic waves using radio and microwave frequencies have been used with machine learning based classification techniques [33,34].…”

Section: Introductionmentioning

confidence: 99%

Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning

Tiitta

Heikkinen

et al. 2020

Sensors

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Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.

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Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning

Cited by 45 publications

References 24 publications

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

Sparse learning of maximum likelihood model for optimization of complex loss function

Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning

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