Method for detecting quality of figs based on near infrared spectrum

A near-infrared spectrum, fig technology, applied in the direction of measuring devices, material analysis through optical means, instruments, etc., can solve the problem of weak fitting ability of partial least squares method, achieve high accuracy, strong fitting ability, high precision effect

Pending Publication Date: 2020-04-10
QILU UNIV OF TECH
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Problems solved by technology

Many scholars at home and abroad have used near-infrared spectroscopy to establish food texture prediction models, most of whic

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  • Method for detecting quality of figs based on near infrared spectrum
  • Method for detecting quality of figs based on near infrared spectrum
  • Method for detecting quality of figs based on near infrared spectrum

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Embodiment 1

[0028] A method for detecting the quality of figs based on near-infrared spectroscopy, comprising:

[0029] (1) Sample selection: select figs without rot or bruises, wash and set aside;

[0030] (2) Spectrum acquisition: scan the sample by a near-infrared spectrometer to obtain its near-infrared spectrum. A fig sample is scanned 1000 times to get the average spectrum, and five different positions are selected for each sample fig to scan to obtain 5 average spectra;

[0031] (3) Spectral preprocessing is carried out to the collected near-infrared spectrum;

[0032] (4) Texture measurement: measure the quality indicators of fig samples, including hardness, elasticity, chewiness, adhesion, elasticity, cohesion, and viscosity;

[0033] (5) Model establishment: the near-infrared spectrum after step (3) pretreatment is correlated with the corresponding quality index in step (4); Adopt random forest algorithm (RF), set up the prediction model to the quality parameter determination ...

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Abstract

The invention relates to the technical field of agricultural product detection, in particular to a method for detecting quality of figs based on near infrared spectrum. A random forest regression algorithm is an ensemble learning algorithm taking a decision tree as a base learner, CART decision trees are trained by using a Bagging ensemble learning technology and forming a forest, the decision trees in the forest are not associated (FIG1), and an average value of the results output by the decision trees is used as a regression result (FIG2), so that the problem of overfitting is solved, and the overall model has relatively high precision and generalization performance; and low RMSEC and RMSEP show that the random forest algorithm is used for predicting the internal quality of the green-peel figs, a good effect is achieved through experiments, and a large amount of experimental data proves that the random forest algorithm has higher fitting capacity than a partial least squares method and is not prone to over-fitting.

Description

technical field [0001] The invention relates to the technical field of agricultural product detection, in particular to a method for detecting the quality of figs based on near-infrared spectroscopy. Background technique [0002] Green fig is one of the earliest fruit tree species improved and cultivated by human beings, and the overall use value of the plant is high. Fig fruit is round in color, bright and juicy, with good taste and high nutritional value. The scale of artificial cultivation in my country has continued to expand in the past 20 years. With the improvement of consumers' living standards, the quality requirements for fruits are also increasing simultaneously. The texture of fruits is more and more concerned by consumers, such as hardness, elasticity, chewiness, adhesion, elasticity, cohesion, and stickiness. Wait. Most of the traditional texture instruments need to destroy the sample to measure the internal texture properties, the measurement efficiency is l...

Claims

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Application Information

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IPC IPC(8): G01N21/359
CPCG01N21/359
Inventor 韩燕苓孙锐余多
Owner QILU UNIV OF TECH
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