基于FT-NIR和电子鼻的苹果水心病无损检测
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摘要
探讨傅里叶变换近红外光谱技术和电子鼻技术应用于苹果水心病检测的可行性。以277个"秦冠"水心病苹果和健康苹果为试材,分别采集每个样本在12 000~4 000 cm-1波数范围的近红外光谱和10个传感器的电子鼻信号,用不同预处理的近红外光谱方法提取主成分建立Fisher判别模型;同时电子鼻结合3种化学计量学的方法进行建模。结果表明,经一阶导数(9点平滑)预处理的近红外光谱,提取前20个主成分建立的Fisher判别模型效果最好,对未知样本的正确判别率达100%;电子鼻分别结合Fisher判别、多层感知器神经网络和径向基函数神经网络判别模型对未知样本的识别率为89.7%、89.5%和85.7%。故利用近红外光谱和电子鼻技术分别结合化学计量学的方法可快速、无损检测苹果的水心病。其中,近红外光谱技术结合Fisher判别对苹果水心病的识别率最高,是一种准确可靠的测定方法。
This study aimed to explore the feasibility of applying near infrared spectroscopy(NIR) and electronic nose(E-nose) for detecting apple watercore. A total of 277 samples of "Qinguan" apples with watercore and healthy apples were tested. NIR spectra of each sample in the range of 12 000 to 4 000 cm-1 and E-nose signals from 10 sensors were collected, The Fisher discriminant model was established with the principal components extracted by different preprocessing methods. Meanwhile, the E-nose data were used for modeling by 3 different chemometric methods. The results indicated that the Fisher discriminant model developed based on the first twenty principal components from the NIR spectra subjected to the first derivative(9-point smoothing) pretreatment worked best with discrimination accuracy rates of 100% for unknown samples. The correct discrimination rates of the discriminant models developed by Fisher discriminant, multilayer perceptron(MLP) neural network and radial basis function(RBF) neural network for unknown samples were 89.7%, 89.5%and 85.7%, respectively. Thus, the combined application of NIR spectroscopy and E-nose with chemometrics can rapidly and nondestructively test watercore apples. NIR spectroscopy combined with Fisher discriminant analysis is an accurate and reliable method for detecting watercore apples with the highest correct recognition rate.
This study aimed to explore the feasibility of applying near infrared spectroscopy(NIR) and electronic nose(E-nose) for detecting apple watercore. A total of 277 samples of "Qinguan" apples with watercore and healthy apples were tested. NIR spectra of each sample in the range of 12 000 to 4 000 cm-1 and E-nose signals from 10 sensors were collected, The Fisher discriminant model was established with the principal components extracted by different preprocessing methods. Meanwhile, the E-nose data were used for modeling by 3 different chemometric methods. The results indicated that the Fisher discriminant model developed based on the first twenty principal components from the NIR spectra subjected to the first derivative(9-point smoothing) pretreatment worked best with discrimination accuracy rates of 100% for unknown samples. The correct discrimination rates of the discriminant models developed by Fisher discriminant, multilayer perceptron(MLP) neural network and radial basis function(RBF) neural network for unknown samples were 89.7%, 89.5%and 85.7%, respectively. Thus, the combined application of NIR spectroscopy and E-nose with chemometrics can rapidly and nondestructively test watercore apples. NIR spectroscopy combined with Fisher discriminant analysis is an accurate and reliable method for detecting watercore apples with the highest correct recognition rate.
引文
[1]杜艳民,王文辉,杭博,等.碳水化合物、矿质元素及活性氧代谢与富士苹果水心发生的关系[J].园艺学报,2015,42(10):2023-2030.DOI:10.16420/j.issn.0513-353x.2015-0181.
[2]刘小勇,张辉元,董铁,等.苹果水心病无损检测与防治研究进展[J].果树学报,2008,25(5):721-726.DOI:10.13925/j.cnki.gsxb.2008.05.021.
[3]SHAHIN M A,TOLLNER E W,MCCLENDON R W.Artificial intelligence classifiers for sorting apples based on watercore[J].Journal of Agricultural Engineering Research,2001,79(3):265-274.DOI:10.1006/jaer.2001.0705.
[4]韩东海,刘新鑫,赵丽丽,等.苹果水心病的光学无损检测[J].农业机械学报,2004,35(5):143-146.DOI:10.3969/j.issn.1000-1298.2004.05.036.
[5]王思玲,蔡骋,马惠玲,等.基于高光谱成像的苹果水心病无损检测[J].北方园艺,2015,39(8):124-130.DOI:10.11937/bfyy.201508033.
[6]CHO B K,CHAYAPRASERT W,STROSHINE R L.Effects of internal browning and watercore on low field(5.4 MHz)proton magnetic resonance measurements of T2 values of whole apples[J].Postharvest Biology&Technology,2008,47(1):81-89.DOI:10.1016/j.postharvbio.2007.05.018.
[7]HERREMANS E,MELADO-HERREROS A,DEFRAEYE T,et al.Comparison of X-ray CT and MRI of watercore disorder of different apple cultivars[J].Postharvest Biology&Technology,2014,87:42-50.DOI:10.1016/j.postharvbio.2013.08.008.
[8]MELADO-HERREROS A,FERNANDEZ-VALLE M E,BARREIRO P.Non-destructive global and localized 2D T1/T2 NMR relaxometry to resolve microstructure in apples affected by watercore[J].Food&Bioprocess Technology,2015,8(1):88-99.DOI:10.1007/s11947-014-1389-4.
[9]BARANOWSKI P,LIPECKI J,MAZUREK W,et al.Detection of watercore in‘Gloster’apples using thermography[J].Postharvest Biology&Technology,2008,47(3):358-366.DOI:10.1016/j.postharvbio.2007.07.014.
[10]王加华,孙旭东,潘璐,等.基于可见/近红外能量光谱的苹果褐腐病和水心鉴别[J].光谱学与光谱分析,2008,28(9):2098-2102.DOI:10.3964/j.issn.1000-0593(2008)09-2098-05.
[11]UPCHURCH B L,THROOP J A,ANESHANSLEY D J.Detecting internal breakdown in apples using interactance measurements[J].Postharvest Biology&Technology,1997,10(1):15-19.DOI:10.1016/S0925-5214(96)00057-9.
[12]CLARK C J,MCGLONE V A,JORDAN R B.Detection of brownheart in‘Braeburn’apple by transmission NIR spectroscopy[J].Postharvest Biology&Technology,2003,28(1):87-96.DOI:10.1016/S0925-5214(02)00122-9.
[13]MCGLONE V A,MARTINSEN P J,CLARK C J,et al.On-line detection of brownheart in Braeburn apples using near infrared transmission measurements[J].Postharvest Biology&Technology,2005,37(2):142-151.DOI:10.1016/j.postharvbio.2005.04.011.
[14]BENNEDSEN B S,PETERSON D L.An optical method for detecting watercore and mealiness in apples[J].Transactions of the ASAE,2005,48(5):1819-1826.
[15]韩东海,刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88.DOI:10.3969/j.issn.1000-1298.2006.06.022.
[16]SHENDEREY C,SHMULEVICH I,ALCHANATIS V,et al.NIRS detection of moldy core in apples[J].Food and Bioprocess Technology,2010,3(1):79-86.DOI:10.1007/s11947-009-0256-1.
[17]李顺峰,张丽华,刘兴华,等.基于主成分分析的苹果霉心病近红外漫反射光谱判别[J].农业机械学报,2011,42(10):158-161.DOI:10.3969/j.issn.1000-1298.2011.10.031.
[18]宫元娟,匡立学,冯叙桥.苹果近红外光谱无损检测技术的研究进展[J].食品与生物技术学报,2012,31(6):570-574.DOI:10.3969/j.issn.1673-1689.2012.06.002.
[19]VANOLI M,RIZZOLO A,GRASSIA M,et al.Studies on classification models to discriminate‘Braeburn’apples affected by internal browning using the optical properties measured by time-resolved reflectance spectroscopy[J].Postharvest Biology&Technolgy,2014,91:112-121.DOI:10.1016/j.postharvbio.2014.01.002.
[20]雷雨,何东健,周兆永,等.苹果霉心病可见/近红外透射能量光谱识别方法[J].农业机械学报,2016,47(4):193-200.DOI:10.6041/j.issn.1000-1298.2016.04.026.
[21]周兆永,何东健,张海辉,等.基于深度信念网络的苹果霉心病病害程度无损检测[J].食品科学,2017,38(14):297-303.DOI:10.7506/spkx1002-6630-201714046.
[22]PATHANGE L P,MALLIKARJUNAN P,MARINI R P,et al.Nondestructive evaluation of apple maturity using an electronic nose system[J].Journal of Food Engineering,2006,77(4):1018-1023.DOI:10.1016/j.jfoodeng.2005.08.034.
[23]HUI G H,WU Y L,YE D D,et al.Fuji apple storage time predictive method using electronic nose[J].Food Analytical Methods,2013,6(1):82-88.DOI:10.1007/s12161-012-9414-6.
[24]李莹,任亚梅,张爽,等.基于电子鼻的苹果低温贮藏时间及品质预测[J].西北农林科技大学学报(自然科学版),2015,43(5):183-191.DOI:10.13207/j.cnki.jnwafu.2015.05.05.11.
[25]邹小波,赵杰文.支持向量机在电子鼻区分不同品种苹果中的应用[J].农业工程学报,2007,23(1):146-149.DOI:10.3321/j.issn:1002-6819.2007.01.027.
[26]张鹏,李江阔,陈绍慧.基于电子鼻判别富士苹果货架期的研究[J].食品工业科技,2015,36(5):272-276.DOI:10.13386/j.issn1002-0306.2015.05.049.
[27]李琦,杨艳菊.基于人工神经网络的苹果气体识别方法研究[J].传感器与微系统,2007,26(9):61-63.DOI:10.3969/j.issn.1000-9787.2007.09.020.
[28]邹小波,赵杰文,潘胤飞,等.基于遗传RBF网络的电子鼻对苹果质量的评定[J].农业机械学报,2005,36(1):61-64.DOI:10.3969/j.issn.1000-1298.2005.01.017.
[29]李光辉,任亚梅,任小林,等.苹果品种及损伤苹果的FT-NIR鉴别研究[J].食品科学,2012,33(16):251-256.
[30]SAEVELS S,LAMMERTYN J,BERNA A Z,et al.Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples[J].Postharvest Biology&Technology,2003,30(1):3-14.DOI:10.1016/S0925-5214(03)00059-0.
[2]刘小勇,张辉元,董铁,等.苹果水心病无损检测与防治研究进展[J].果树学报,2008,25(5):721-726.DOI:10.13925/j.cnki.gsxb.2008.05.021.
[3]SHAHIN M A,TOLLNER E W,MCCLENDON R W.Artificial intelligence classifiers for sorting apples based on watercore[J].Journal of Agricultural Engineering Research,2001,79(3):265-274.DOI:10.1006/jaer.2001.0705.
[4]韩东海,刘新鑫,赵丽丽,等.苹果水心病的光学无损检测[J].农业机械学报,2004,35(5):143-146.DOI:10.3969/j.issn.1000-1298.2004.05.036.
[5]王思玲,蔡骋,马惠玲,等.基于高光谱成像的苹果水心病无损检测[J].北方园艺,2015,39(8):124-130.DOI:10.11937/bfyy.201508033.
[6]CHO B K,CHAYAPRASERT W,STROSHINE R L.Effects of internal browning and watercore on low field(5.4 MHz)proton magnetic resonance measurements of T2 values of whole apples[J].Postharvest Biology&Technology,2008,47(1):81-89.DOI:10.1016/j.postharvbio.2007.05.018.
[7]HERREMANS E,MELADO-HERREROS A,DEFRAEYE T,et al.Comparison of X-ray CT and MRI of watercore disorder of different apple cultivars[J].Postharvest Biology&Technology,2014,87:42-50.DOI:10.1016/j.postharvbio.2013.08.008.
[8]MELADO-HERREROS A,FERNANDEZ-VALLE M E,BARREIRO P.Non-destructive global and localized 2D T1/T2 NMR relaxometry to resolve microstructure in apples affected by watercore[J].Food&Bioprocess Technology,2015,8(1):88-99.DOI:10.1007/s11947-014-1389-4.
[9]BARANOWSKI P,LIPECKI J,MAZUREK W,et al.Detection of watercore in‘Gloster’apples using thermography[J].Postharvest Biology&Technology,2008,47(3):358-366.DOI:10.1016/j.postharvbio.2007.07.014.
[10]王加华,孙旭东,潘璐,等.基于可见/近红外能量光谱的苹果褐腐病和水心鉴别[J].光谱学与光谱分析,2008,28(9):2098-2102.DOI:10.3964/j.issn.1000-0593(2008)09-2098-05.
[11]UPCHURCH B L,THROOP J A,ANESHANSLEY D J.Detecting internal breakdown in apples using interactance measurements[J].Postharvest Biology&Technology,1997,10(1):15-19.DOI:10.1016/S0925-5214(96)00057-9.
[12]CLARK C J,MCGLONE V A,JORDAN R B.Detection of brownheart in‘Braeburn’apple by transmission NIR spectroscopy[J].Postharvest Biology&Technology,2003,28(1):87-96.DOI:10.1016/S0925-5214(02)00122-9.
[13]MCGLONE V A,MARTINSEN P J,CLARK C J,et al.On-line detection of brownheart in Braeburn apples using near infrared transmission measurements[J].Postharvest Biology&Technology,2005,37(2):142-151.DOI:10.1016/j.postharvbio.2005.04.011.
[14]BENNEDSEN B S,PETERSON D L.An optical method for detecting watercore and mealiness in apples[J].Transactions of the ASAE,2005,48(5):1819-1826.
[15]韩东海,刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88.DOI:10.3969/j.issn.1000-1298.2006.06.022.
[16]SHENDEREY C,SHMULEVICH I,ALCHANATIS V,et al.NIRS detection of moldy core in apples[J].Food and Bioprocess Technology,2010,3(1):79-86.DOI:10.1007/s11947-009-0256-1.
[17]李顺峰,张丽华,刘兴华,等.基于主成分分析的苹果霉心病近红外漫反射光谱判别[J].农业机械学报,2011,42(10):158-161.DOI:10.3969/j.issn.1000-1298.2011.10.031.
[18]宫元娟,匡立学,冯叙桥.苹果近红外光谱无损检测技术的研究进展[J].食品与生物技术学报,2012,31(6):570-574.DOI:10.3969/j.issn.1673-1689.2012.06.002.
[19]VANOLI M,RIZZOLO A,GRASSIA M,et al.Studies on classification models to discriminate‘Braeburn’apples affected by internal browning using the optical properties measured by time-resolved reflectance spectroscopy[J].Postharvest Biology&Technolgy,2014,91:112-121.DOI:10.1016/j.postharvbio.2014.01.002.
[20]雷雨,何东健,周兆永,等.苹果霉心病可见/近红外透射能量光谱识别方法[J].农业机械学报,2016,47(4):193-200.DOI:10.6041/j.issn.1000-1298.2016.04.026.
[21]周兆永,何东健,张海辉,等.基于深度信念网络的苹果霉心病病害程度无损检测[J].食品科学,2017,38(14):297-303.DOI:10.7506/spkx1002-6630-201714046.
[22]PATHANGE L P,MALLIKARJUNAN P,MARINI R P,et al.Nondestructive evaluation of apple maturity using an electronic nose system[J].Journal of Food Engineering,2006,77(4):1018-1023.DOI:10.1016/j.jfoodeng.2005.08.034.
[23]HUI G H,WU Y L,YE D D,et al.Fuji apple storage time predictive method using electronic nose[J].Food Analytical Methods,2013,6(1):82-88.DOI:10.1007/s12161-012-9414-6.
[24]李莹,任亚梅,张爽,等.基于电子鼻的苹果低温贮藏时间及品质预测[J].西北农林科技大学学报(自然科学版),2015,43(5):183-191.DOI:10.13207/j.cnki.jnwafu.2015.05.05.11.
[25]邹小波,赵杰文.支持向量机在电子鼻区分不同品种苹果中的应用[J].农业工程学报,2007,23(1):146-149.DOI:10.3321/j.issn:1002-6819.2007.01.027.
[26]张鹏,李江阔,陈绍慧.基于电子鼻判别富士苹果货架期的研究[J].食品工业科技,2015,36(5):272-276.DOI:10.13386/j.issn1002-0306.2015.05.049.
[27]李琦,杨艳菊.基于人工神经网络的苹果气体识别方法研究[J].传感器与微系统,2007,26(9):61-63.DOI:10.3969/j.issn.1000-9787.2007.09.020.
[28]邹小波,赵杰文,潘胤飞,等.基于遗传RBF网络的电子鼻对苹果质量的评定[J].农业机械学报,2005,36(1):61-64.DOI:10.3969/j.issn.1000-1298.2005.01.017.
[29]李光辉,任亚梅,任小林,等.苹果品种及损伤苹果的FT-NIR鉴别研究[J].食品科学,2012,33(16):251-256.
[30]SAEVELS S,LAMMERTYN J,BERNA A Z,et al.Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples[J].Postharvest Biology&Technology,2003,30(1):3-14.DOI:10.1016/S0925-5214(03)00059-0.