中國農業科學 ?? 2015, Vol. 48 ?? Issue (20): 4111-4119.doi: 10.3864/j.issn.0578-1752.2015.20.012

? 園藝·貯藏·保鮮·加工 ? 上一篇    下一篇

基于近紅外光譜的紐荷爾臍橙產地識別研究

廖秋紅1,2,何紹蘭1,謝讓金1,錢春2,胡德玉1,2,呂強1,易時來1,鄭永強1,鄧烈1

 
  

  1. 1中國農業科學院柑桔研究所/西南大學柑桔研究所/國家柑桔工程技術研究中心,重慶 400712
    2西南大學園藝園林學院,重慶 400715
  • 收稿日期:2015-03-15 出版日期:2015-10-20 發布日期:2015-10-20
  • 通訊作者: 鄧烈,E-mail:[email protected] E-mail:[email protected]
  • 作者簡介:廖秋紅,E-mail:[email protected]
  • 基金資助:
    國家“863”計劃(2012AA101904)、國家國際科技合作專項(2013DFA11470)、重慶市科技支撐項目(cstc2014fazktpt80015)

Study on Producing Area Classification of Newhall Navel Orange Based on the Near Infrared Spectroscopy

LIAO Qiu-hong1,2, HE Shao-lan1, XIE Rang-jin1, QIAN Chun2, HU De-yu1,2, LÜ Qiang1, YI Shi-lai1, ZHENG Yong-qiang1, DENG Lie1   

  1. 1Citrus Research Institute, Southwest University/Chinese Academy of Agricultural Sciences/ National Engineering Technology Research Center for Citrus, Chongqing 400712
    2College of Horticulture and Landscape Architecture, Southwest University, Chongqing 400715
  • Received:2015-03-15 Online:2015-10-20 Published:2015-10-20

摘要: 【目的】中國柑橘產區分布廣、生態類型復雜,不同產地紐荷爾臍橙果實品質和市場效應具有較大差異。研究基于近紅外光譜技術的柑橘產地識別技術,利于不同柑橘產地果品的識別和鑒偽。【方法】從中國南方17個紐荷爾臍橙主要產地選擇代表性成年果園,分別采摘成熟鮮果樣品100個。利用SupNIR-1500近紅外分析儀采集臍橙果實赤道部、肩部表面以及果汁濾液的近紅外反射光譜,光譜波長范圍為1 000—2 499 nm。采用主成分分析法對原始光譜數據進行預處理,提取近紅外光譜的特征信息以降低數據集維度以及噪聲。研究人工神經網絡理論,構建由一個輸入層、一個具有非線性激勵函數的隱藏層和一個輸出層組成的典型的3層人工神經網絡識別模型。研究由徑向基函數作為核函數、以光譜主成分作為輸入的支持向量機模型,構建由126個分類器組成的一對一擴展支持向量機模型。研究遺傳算法優異的自然選擇特性,利用遺傳算法從光譜主成分中選擇出最優的特征基因子集作為支持向量機的輸入,構建遺傳算法-支持向量機模型。利用3種模型分別對果汁濾液的近紅外反射光譜數據進行分類,從而實現產地識別測試,并根據產地識別精度篩選出最優的產地識別模型。進一步對比該最優識別模型對果實赤道部、肩部反射光譜數據的識別精度,從而確定識別精度最高的光譜數據采集源。【結果】利用所建立的3層人工神經網絡模型對紐荷爾臍橙果汁濾液的近紅外光譜進行產地識別測試,確定當輸入神經元數量為11、隱藏神經元數量為13時,模型對果實產地識別的最佳精度達81.45%。采用一對一擴展方式建立支持向量機產地識別模型,研究確定采用徑向基函數作為核函數,當主成分數量為20時,臍橙產地識別精度最高可達86.98%。測試利用遺傳算法-支持向量機混合模型進行臍橙產地分類識別,確定當種群數量為200、遺傳代數為100、交叉概率0.7、突變概率0.01時,遺傳算法選擇出最優的基因子集進行產地識別,遺傳算法-支持向量機模型的產地識別精度最高可達89.72%,優于人工神經網絡分類模型和支持向量機分類模型的產地識別精度。進一步利用遺傳算法-支持向量機產地識別模型對果實赤道部及肩部的果面反射光譜進行產地識別測試,得到對應的最高識別精度分別為80.00%和69.00%。【結論】遺傳算法-支持向量機模型對果汁反射近紅外光譜進行產地識別精度最高,優于人工神經網絡模型和支持向量機模型。該模型對果實赤道部反射光譜進行分類的精度次于果汁濾液反射光譜但優于果實肩部反射光譜,因此,可利用赤道部的反射光譜實現非破壞性果實產地分類識別

關鍵詞: 紐荷爾臍橙, 產地識別, 近紅外光譜, 主成分分析, 人工神經網絡, 支持向量機, 遺傳算法

Abstract: 【Objective】Newhall navel orange (Citrus sinensis L.) fruits from different producing areas in China, exhibit contrasting quality and market values, due to wide-spreading location of various ecologies. Developing a recognition method based on Near-Infrared (NIR) spectroscopy is very important to identify and distinguish fruits from different producing areas. 【Method】One representative orchard was selected from 17 main producing areas distributed throughout southern China, from which one 100 Newhall navel orange samples were collected. NIR spectra were collected with a SupNIR-1500 spectrograph from the surface of fruit equator and shoulder and the filtered juice for each sample, which were further preprocessed through principal component analysis (PCA) for reduced dimensions and noise. By studying artificial neural network (ANN), a classic three-layer ANN model was established with an input layer, a hidden layer of non-linear activation function and an output layer. By studying support vector machine (SVM) with the radial basis function (RBF) being the kernel function and the principal components of NIR spectra being the input, a one-to-one extended SVM model was established with 126 classifiers. Gene algorithm (GA) with excellent natural selection was used to identify the best Genetic character subset from the principal components as inputs of a SVM classifier, thus a GA-optimized SVM model was composed. These three models were used to classify the NIR spectra of filtered juice, thus the production areas of the oranges, the classification accuracies of which decided the best classifier. Furthermore, the accuracies of the best classifier were tested with the NIR spectra from fruit equator and shoulder surface being the inputs. As a comparison, the best NIR spectra could be identified. 【Result】 Producing area classification was implemented with the three-layer ANN classifier with NIR spectra of Newhall orange juice as the input, where the classifying accuracy reached up to 81.45% when there were 11 input neurons and 13 hidden neurons. The studied one-to-one extended SVM classifier with radial basis function being the core function, exhibited higher accuracy of 86.98% when the number of PC was 20, better than ANN classifier. For GA-SVM classifier took into account the interaction of individual inputs, where the PCA-processed results were optimized by GA. During the experiments, classification accuracy hit 89.72% when the population, generation, mating probability, and mutation probability were 200, 100, 0.7 and 0.01 respectively, surpassed ANN and SVM classifier. Subsequent research found the highest accuracy of GA-SVM classifier was 80% when taking the spectra from the fruit equator, and 69% from the shoulder, not good enough as that of orange juice.【Conclusion】Considering the accuracy, GA-SVM classifier was regarded with the most excellence among three investigated classifiers. Spectra of orange juice were selected as the best data to analyze origins traceability. Accuracy of spectra of fruit equator was inferior to juice but superior to the shoulder, thus had the potential for non-destructive origins classification.

Key words: Newhall Navel orange, producing area recognition, near-infrared spectroscopy, principal components analysis, artificial neural network, support vector machine, genetic algorithm

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