Prediction of egg storage time and yolk index based on electronic nose combined with chemometric methods

Li, Jiating; Zhu, Susu; Jiang, Shui; Wang, Jun

doi:10.1016/j.lwt.2017.04.070

Cited by 45 publications

(25 citation statements)

References 23 publications

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“…Widely varying methods have been proposed and employed for the evaluation of freshness or incipient spoilage in food that are high in protein. Li et al [ 212 ] used BPNN and SVM to build prediction models of yolk index with a dataset obtained by electronic nose. The SVM model with reduction of dataset by independent component analysis (ICA) showed better results than BPNN.…”

Section: Application Of Non-linear Methods On Food Groupsmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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Section: Application Of Non-linear Methods On Food Groupsmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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“…And the number of output neurons usually takes the value of the dimension of the output vector. There is no definite standard for choosing the number of hidden layer neurons, so it is often decided by traversing within the set range [ 7 , 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

Liu

Zhu

Feng

et al. 2018

Sensors

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Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

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“…In the detection of freshness, the methods of Haugh unit and egg yolk index are more commonly used for destructive testing. Nondestructive testing methods include electronic nose [2] and dielectric [3] measurement methods. In the egg yolk index, spectroscopy is rarely used as a detection method.…”

Section: Extended Abstractmentioning

confidence: 99%