基于人工神经网络的毛精纺纱线质量预报技术

介绍了毛精纺纺纱过程与人工神经网络的特点 ,提出人工神经网络在纺纱质量预报中的工作原理与实现方法 ,并提供了国内外的应用实例 ,指出人工神经网络技术在毛精纺纱线质量预报中的广泛应用前景。     

A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

This study focused on predicting tensile properties of PES/CV/PAN blended Open-End Rotor yarns. The effective factors were fiber blend ratios (six stages from 0 to 100%), linear density (three count levels), mixing method (carding machine and drawframe), and number of passages in drawframe (one and two times) as production parameters. We performed a stepwise multiple linear regression (MLR) analysis and established an artificial neural network (ANN) model that trained with backpropagation rule as Levenberg–Marquardt. Then, we conducted a comparative analysis for both models in terms of prediction performance. As a result, ANN has given a slightly better prediction values than MLR for breaking strength but significantly better prediction values for breaking elongation.     

Characterisation of white vinegars of different sources with artificial neural networks

Wine and cider vinegars currently attract growing interest from consumers, giving rise to a consequent increase in supply. A full appreciation of their quality is only possible, however, through recognition of their superior quality when compared with vinegars produced from raw materials of inferior quality. Therefore, it is necessary to pinpoint the parameters that define the quality of these products. Chemico-physical and sensory analysis has been used to draw up artificial neural networks (ANNs), on the basis of a vast sampling of vinegars from various countries, produced from a variety of raw materials, that was already subjected to multivariate statistical analysis. Among the chemical parameters, polyalcohols and other elements such as pH, tartaric acid and proline proved to be highly reliable, whereas other volatile substances and the results of sensory analysis were not very discriminating and could not be used to re-classify samples of unknown origin. The positive results obtained indicate that ANNs are a powerful mathematical tool, since they can be used to construct models that predict the botanical origin of the product and to re-classify samples of unknown origin, without any initial restrictive hypothesis. © 1998 Society of Chemical Industry.     

An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks

The ability to predict meat drip loss by using either near infrared spectra (SPECTRA) or different meat quality (MQ) measurements, such as pH₂₄, Minolta L^∗, a^∗, b^∗, along with different chemometric approach, was investigated. Back propagation (BP) and counter propagation (CP) artificial neural networks (ANN) were used and compared to PLS (partial least squares) regression. Prediction models were created either by using MQ measurements or by using NIR spectral data as independent predictive variables. The analysis consisted of 312 samples of longissimus dorsi muscle. Data were split into training and test set using 2D Kohonen map. The error of drip loss prediction was similar for ANN (2.2–2.6%) and PLS models (2.2–2.5%) and it was higher for SPECTRA (2.5–2.6%) than for MQ (2.2–2.3%) based models. Nevertheless, the SPECTRA based models gave reasonable prediction errors and due to their simplicity of data acquisition represent an acceptable alternative to classical meat quality based models.     

配毛中原料品质的BP神经网络预测研究

用BP神经网络方法对配毛时所需的羊毛原料品质进行预测，阐述了利用BP神经网络预测羊毛品质的工作原理，给出了羊毛品质预测系统的BP神经网络模型，并得出了试验结论。     

A critical review on the use of artificial neural networks in olive oil production,characterization and authentication

Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.     

Rapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass spectrometry and artificial neural networks

Curie-point pyrolysis mass spectra were obtained from a variety of extra-virgin olive oils, prepared from various cultivars using several mechanical treatments. Some of the oils were adulterated (according to a double-blind protocol) with different amounts of seed oils (50–500 ml of soya, sunflower, peanut, corn or rectified olive oils per litre of mixed oil). Canonical variates analysis indicated that the major source of variation between the pyrolysis mass spectra was due to differences between the cultivars. rather than whether the oils had been adulterated. However, artificial neural networks could be trained (using the back-propagation algorithm) successfully to distinguish virgin oils from those which had been adulterated.     

人工神经网络-遗传算法优化酶法提取绿豆渣水溶性纤维素工艺研究

探讨了人工神经网络和遗传算法在酶法提取绿豆渣水溶性纤维素工艺优化方面的应用。基于RBF神经网络建立绿豆中的水溶性纤维素提取率预测模型,并利用遗传算法对影响提取率的主要因素进行寻优。由实际数据得出仿真结果,并通过验证实验表明该方法有效。     

Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks

An attempt to classify dry-cured hams according to the maturation time on the basis of near infrared (NIR) spectra was studied. The study comprised 128 samples of biceps femoris (BF) muscle from dry-cured hams matured for 10 (n = 32), 12 (n = 32), 14 (n = 32) or 16 months (n = 32). Samples were minced and scanned in the wavelength range from 400 to 2500 nm using spectrometer NIR System model 6500 (Silver Spring, MD, USA). Spectral data were used for i) splitting of samples into the training and test set using 2D Kohonen artificial neural networks (ANN) and for ii) construction of classification models using counter-propagation ANN (CP-ANN). Different models were tested, and the one selected was based on the lowest percentage of misclassified test samples (external validation). Overall correctness of the classification was 79.7%, which demonstrates practical relevance of using NIR spectroscopy and ANN for dry-cured ham processing control.     

Quality evaluation of Hanyuan Zanthoxylum bungeanum Maxim. Using computer vision system combined with artificial neural network: A novel method

A novel technology based on computer vision system (CVS) and artificial neural network (ANN) was developed for the quality evaluation of Hanyuan Zanthoxylum bungeanum Maxim (HZB). The quality evaluation of HZB mainly depended on its colour, odour substances, and impurities. In this study, the contents of volatile oil (VOC), total alkylamides (TALC) and impurities (IMC) were determined and used as indices for quality control of HZB. Furthermore, CVS was also performed to determine the colour parameters (RGB values) and further transforms to CIE L*, a^*, and b*. Then, ANN was carried out to analyse the correlations between colour values obtained by CVS and quality parameters of HZB (VOC, TALC, and IMC). Higher performance and stability were presented by using CVS for determining the coloristic values of HZB. In addition, the present results also showed that the established method based on ANN could be used to predict the VOC, TALC, and IMC of HZB with the R² values of 0.9991, 0.9995, and 0.9998, respectively. This novel technology based on CVS combined with ANN could be used for the rapid, non-destructive, and effective evaluation of the quality of HZB.     

Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models

Introduction

The antioxidant properties of essential oils (EOs) have been on the centre of intensive research for their potential use as preservatives or nutraceuticals by the food industry. The enormous amount of information already generated on this subject provides a rich field for data-miners as it is conceivable, with suitable computational techniques, to predict the antioxidant capacity of any essential oil just by knowing its particular chemical composition. To accomplish this task we here report on the design, training and validation of an Artificial Neural Network (ANN) able to predict the antioxidant activity of EOs of known chemical composition.

Methods

A multilayer ANN with 30 input neurons, 42 in hidden layers (20 → 15 → 7) and one neuron in the output layer was developed and run using Fast Artificial Neural Network software. The chemical composition of 32 EOs and their antioxidant activity in the DPPH and linoleic acid models were extracted from the scientific literature and used as input values. From the initial set of around 80 compounds present in these EOs, only 30 compounds with relevant antioxidant capacity were selected to avoid excessive complexity of the neural network and minimise the associated structural problems.

Results and discussion

The ANN could predict the antioxidant capacities of essential oils of known chemical composition in both the DPPH and linoleic acid assays with an average error of only 3.16% and 1.46%, respectively. We also discuss on the contribution of different compounds to these chemical activities.

Conclusions

These results confirm that artificial neural networks are reliable, fast and cheap tools for predicting antioxidant activity of essential oils from some of its components and can be used to model biochemical properties of complex natural products including the prediction of parameters associated with nutraceutical properties of food ingredients.     

Estimation of oxygen uptake rate of tomato (Lycopersicon esculentum Mill.) fruits by artificial neural networks modelled using near-infrared spectral absorbance and fruit mass

The oxygen uptake rate of tomato fruits was estimated by an artificial neural network (ANN) model using near-infrared (NIR) spectral absorbance and fruit mass. The absorption peak apex from cytochrome c oxidase (COX) was confirmed at 841 nm for mitochondrial preparation and at 833 nm for intact fruits. The results of a proteome analysis that detected the putative COX subunit II PS17 from the mitochondrial preparation biochemically supported the presence of the absorption peak due to COX. An ANN model for estimating O₂ uptake rate was developed from the absorbance data at 11 wavelengths from 645 to 979 nm including 833 nm and fruit mass as input variables. The O₂ uptake rate was estimated by the proposed model with a correlation coefficient of 0.79 and a standard error of prediction of 0.091 mmol kg⁻¹ h⁻¹. This method may be effective for rapid estimation of shelf life and physiological activity of tomato fruits.     

The effect of prior storage on the potential of potato tubers (Solanum tuberosum L) to accumulate glycoalkaloids and chlorophylls during light exposure,including artificial neural network modelling

Potato tubers of four varieties (Brodick, King Edward, Pentland Dell and Record) were stored under commercial conditions and exposed to light for up to 10 days after 0, 10, 20 and 30 weeks. These were analysed for photosynthetic pigment and glycoalkaloid content. There was no significant alteration in either tuber chlorophyll or glycoalkaloid content during dark storage. All four varieties greened in response to light exposure, but only three exhibited a significant increase in glycoalkaloid concentrations during this exposure. Storage duration did not significantly affect pigment accumulation. However, there was a marked effect of storage on the extent of glycoalkaloid accumulation. Tubers of all four varieties stored for more than 10 weeks did not accumulate glycoalkaloids to the same extent as fresh tubers. Indeed, Brodick and Record did not accumulate any glycoalkaloids in response to light after 30 weeks of storage. A number of artificial neural network models of the results were produced. These accurately modelled cultivars individually, but a model encompassing all the data was not successful at predicting cultivar differences. © 1999 Society of Chemical Industry     

Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis

Fourier transform mid-infrared (FT-IR) spectroscopy was evaluated as a tool to discriminate between carcasses of suckling lambs according to the rearing system. Fat samples (39 perirenal and 67 omental) were collected from carcasses of lambs from up to three sheep dairy farms, reared on either ewes milk (EM) or milk replacer (MR). Fatty acid composition of the samples from each fat deposit was first analyzed and, when discriminant-partial least squares regression (PLS) was applied, a perfect discrimination between rearing systems could be established. Additionally, FT-IR spectra of fat samples were obtained and discriminant-PLS and artificial neural network (ANN) based analysis were applied to data sets, the latter using principal component analysis (PCA) or support vector machines (SVM) as processing procedure. Perirenal fat samples were perfectly discriminated from their FT-IR spectra. However, analysis of omental fat showed misclassification rates of 9–13%, with the ANN approach showing a higher discrimination power.     

Using <Superscript>1</Superscript>H and <Superscript>13</Superscript>C NMR techniques and artificial neural networks to detect the adulteration of olive oil with hazelnut oil

The lack of any official analytical method to detect the adulteration of olive oil with a low percentage of hazelnut oil is explained by the similarities in the chemical compositions of both kinds of oils. To counter this problem, an artificial neural network based on ¹H-NMR and ¹³C-NMR data has been developed to detect olive oil adulteration, and the results from this ANN are presented here. A training set consisting of hazelnut oils, pure olive oils, and olive oils blended with 2–20% hazelnut oils was used to design and train a multilayer perceptron with 100% correct classifications. This mathematical model was also validated using an external validation set of blend samples (3–15%) and genuine samples. The detection limit of the model was around 8%.