Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest

Liu, Jun; Sun, Siqi; Tan, Zhenglin; Liu, Yang

doi:10.1016/j.saa.2020.118718

Cited by 32 publications

(12 citation statements)

References 21 publications

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“…Using the bagging method for developing an RF model highly reduces the variance and the prediction bias, either underestimation or overestimation, of the output target. Furthermore, RF works perfectly with non-linear data [ 36 ]. In this paper, the random forest decision trees were built using 100 trees in each forest, with 13 as the depth of each tree.…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Badarinath

Chierichetti

Kakhki

2021

Sensors

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Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Badarinath

Chierichetti

Kakhki

2021

Sensors

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“…Kartakoullis et al [ 29 ] (2019) showed that fat and moisture content can be detected by building a random forest model using a full spectrum over a wide temperature range using a smartphone-based spectrometer with a good detection accuracy (RPD > 7), comparable to the accuracy of benchtop spectrometers. The results of Liu et al [ 36 ] (2020) demonstrated that NIR spectroscopy combined with the random forest algorithm is a quick and non-destructive method used to detect sunset yellow in cream. Du et al [ 37 ] (2021) demonstrated that the ADA content in flour can be precisely determined by NIR combined with the random forest algorithm.…”

Section: The Related Workmentioning

confidence: 99%

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Tan

You

Tian

et al. 2022

Sensors

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Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

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“…The research materials involved oral English recordings, business English courses in some universities, and the real data on students' role-playing. Liu et al [31] proposed a method of non-destructive detection of sunset yellow in cream, which is based on infrared spectroscopy and interval random forest. The research results show showed that interval random forest can find the optimal sub-interval quickly and non-destructively, thus confirming the prediction ability of the model.…”

Section: Current Research Statusmentioning

confidence: 99%

Research on College English Teaching Model Based on Decision Trees

Wu¹,

Nagaraj²

2021

Intelligent Automation &Amp; Soft Computing

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English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest model is constructed using a decision trees selection method, which focuses on 19 attributes of the English teaching model. Results show, to begin with, that the indigenous teaching plan and environment fail to adapt to the pace of knowledge iteration in the era of big data. Moreover, interactions between teachers and students appear to be shallow with little constructive interaction, causing a decline in the relationship between teachers and students. Last, there is still no signs of any legitimate construction in terms of in-person English teaching, relativity attribute, corpus and platform. Therefore, this paper has proposed a new English teaching model to adapt to the current college English teaching environment. The experimental results show that the method is effective and feasible for the current college English teaching.

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Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest

Cited by 32 publications

References 21 publications

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Research on College English Teaching Model Based on Decision Trees

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