基于高光譜技術(shù)的豬肉肌紅蛋白含量無(wú)損檢測(cè)

王立舒，胡金耀，房俊龍，陳 曦，李 闖

基于高光譜技術(shù)的豬肉肌紅蛋白含量無(wú)損檢測(cè)

王立舒，胡金耀，房俊龍※，陳 曦，李 闖

（東北農(nóng)業(yè)大學(xué)電氣與信息學(xué)院，哈爾濱 150030）

無(wú)損檢測(cè)；光譜特征；高光譜圖片；卷積神經(jīng)網(wǎng)絡(luò)；卷積自編碼器

0 引 言

中國(guó)是豬肉生產(chǎn)和消費(fèi)第一大國(guó)[1]，豬肉在市場(chǎng)銷售過(guò)程中，色澤是影響消費(fèi)者購(gòu)買行為的主要因素[2]。生鮮肉色澤主要由肉中肌紅蛋白相對(duì)含量及存在狀態(tài)決定。隨著冷藏時(shí)間延長(zhǎng)，脫氧肌紅蛋白與氧合肌紅蛋白逐步氧化為高鐵肌紅蛋白，使生鮮豬肉逐漸由鮮紅色轉(zhuǎn)變?yōu)榧t褐色[3]，影響豬肉在市場(chǎng)上的銷售?？焖贆z測(cè)肌紅蛋白相對(duì)含量，及時(shí)調(diào)節(jié)影響肌紅蛋白改變的因素，在保證豬肉質(zhì)量前提下，使豬肉維持鮮紅色對(duì)肉品銷售至關(guān)重要。目前對(duì)肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)檢測(cè)主要有分光光度法、電化學(xué)法等，這幾種方法準(zhǔn)確度高，但對(duì)樣本破壞性大，操作過(guò)程復(fù)雜[4]，因此實(shí)現(xiàn)對(duì)豬肉肌紅蛋白快速無(wú)損檢測(cè)仍是值得研究的問(wèn)題。

目前，光譜技術(shù)可對(duì)樣本內(nèi)部特征快速檢測(cè)，已經(jīng)廣泛應(yīng)用于土壤[5-7]、農(nóng)產(chǎn)品[8-9]、食品[10]等領(lǐng)域。高光譜成像能同時(shí)獲取樣本圖像與光譜信息，基于圖譜特征建立數(shù)學(xué)模型，能實(shí)現(xiàn)對(duì)樣本快速分類與提高預(yù)測(cè)精度[11]。孫俊等[12]利用堆疊自動(dòng)編碼器（Stacked Auto Encoder，SAE）提取不同放置時(shí)間大米光譜與圖像融合特征，建立支持向量回歸（Support Vector Regression，SVR）預(yù)測(cè)模型，實(shí)現(xiàn)對(duì)大米蛋白質(zhì)含量在線檢測(cè)。王彩霞等[13]采用連續(xù)投影算法、變量組合集群分析法提取特征光譜與第一主成分圖像紋理特征建立偏最小二乘回歸（Partial Least Squares Regression，PLSR）預(yù)測(cè)模型，實(shí)現(xiàn)對(duì)羊肉中飽和脂肪酸含量預(yù)測(cè)。翁士狀等[14]利用卷積神經(jīng)網(wǎng)絡(luò)（Convolutional Neural Network，CNN）模型融合大米的圖譜特征，實(shí)現(xiàn)對(duì)大米品質(zhì)的無(wú)損檢測(cè)。由于高光譜圖像數(shù)據(jù)特征相關(guān)性強(qiáng)，冗余度高，采用線性方法處理高光譜數(shù)據(jù)，將直接影響模型預(yù)測(cè)精度。SAE具有非線性深層網(wǎng)絡(luò)結(jié)構(gòu)，能實(shí)現(xiàn)對(duì)輸入數(shù)據(jù)特征提取[15]，使其在故障檢測(cè)[16]、光譜特征提取[17]、圖像分類[18]等領(lǐng)域廣泛應(yīng)用。對(duì)于大容量的數(shù)據(jù)樣本，因SAE層數(shù)增加導(dǎo)致特征提取時(shí)間增加。深度學(xué)習(xí)技術(shù)快速發(fā)展[19]，把SAE中全連接網(wǎng)絡(luò)結(jié)構(gòu)替換成卷積神經(jīng)網(wǎng)絡(luò)，使用卷積自動(dòng)編碼器（Convolutional Auto Encoder，CAE）提取高光譜圖像數(shù)據(jù)特征，能獲得魯棒性強(qiáng)、可判別性高的光譜與圖像的深度特征。這種特征提取方式可解決線性方法提取特征能力不足與SAE計(jì)算速度過(guò)慢等問(wèn)題。

目前CNN在機(jī)器視覺(jué)領(lǐng)域表現(xiàn)優(yōu)異[20]，基于特征建立CNN預(yù)測(cè)模型相比與傳統(tǒng)機(jī)器學(xué)習(xí)預(yù)測(cè)模型如SVR、PLSR等，能減少對(duì)數(shù)據(jù)預(yù)處理并提高模型預(yù)測(cè)精度[21]。本文采用CNN對(duì)光譜特征與圖像特征及圖-譜融合特征分別建立預(yù)測(cè)模型，實(shí)現(xiàn)對(duì)豬肉脫氧肌紅蛋白、氧合肌紅蛋白、高鐵肌紅蛋白相對(duì)含量的無(wú)損檢測(cè)，以期為生鮮肉類品質(zhì)在線檢測(cè)提供技術(shù)支持。

1 材料與方法

1.1 樣本制備與處理

試驗(yàn)豬肉樣本品種為東北農(nóng)業(yè)大學(xué)三花豬肉。該豬已達(dá)到7個(gè)月的出欄期，屠宰后經(jīng)過(guò)24 h排酸。獲取總質(zhì)量為3 kg的里脊肉樣本并剔除周邊脂肪與結(jié)締組織，用保鮮袋密封包裝迅速運(yùn)回?zé)o損檢測(cè)實(shí)驗(yàn)室。先把豬肉樣本切成10份形狀為10 cm×10 cm×1 cm (長(zhǎng)×寬×高)立方體，然后把每份樣品切成2 cm×2 cm×1 cm的立方體，共計(jì)240個(gè)樣本。將所用樣品置于4 ℃恒溫恒濕箱中放置0～5 d，每天取出40個(gè)樣本送往高光譜實(shí)驗(yàn)室進(jìn)行高光譜圖像采集與肌紅蛋白含量測(cè)量。針對(duì)不同部位的豬肉該檢測(cè)方法仍有適用性，但對(duì)不同品種的豬肉，仍需要建立新的光譜圖像數(shù)據(jù)庫(kù)。

1.2 肌紅蛋白測(cè)量

本次研究中對(duì)肌紅蛋白含量測(cè)量參考Krzywick[22]分光光度法，得出樣本測(cè)量值。將磨碎豬肉樣品5 g與25 mL磷酸鈉緩沖液（0.04 mol/L，pH值為6.8）混合，然后用勻漿器以10 000 r/min均質(zhì)30 s。將均質(zhì)液放置4 ℃恒溫恒濕箱保存1 h后取出。以4 500 r/min離心20 min后過(guò)濾上清液。用分光光度計(jì)分別在525、545、565和572 nm測(cè)定濾液吸光度值。由吸光度值計(jì)算脫氧肌紅蛋白、氧合肌紅蛋白、高鐵肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)，計(jì)算公式如下：

式中1、2、3分別為572與525 nm、565與525 nm、545 與525 nm吸光度比值。DeoMb為脫氧肌紅蛋白質(zhì)量分?jǐn)?shù)（%），OxyMb為氧合肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)（%），MetMb為高鐵肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)（%）。貯藏期間生鮮豬肉肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)的變化趨勢(shì)如圖1所示。

由圖1可知，在0～5 d試驗(yàn)周期內(nèi)脫氧肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)下降緩慢，氧和肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)明顯下降，高鐵肌紅蛋白相對(duì)質(zhì)量分?jǐn)?shù)先下降再上升。隨冷藏時(shí)間延長(zhǎng)，生鮮豬肉發(fā)生褐變最終腐敗變質(zhì)。

1.3 高光譜圖像采集

高光譜圖像采集在東北農(nóng)業(yè)大學(xué)電氣與信息學(xué)院高光譜圖像處理實(shí)驗(yàn)室進(jìn)行，高光譜成像系統(tǒng)硬件部分如圖2所示。該硬件系統(tǒng)主要由高光譜成像儀（HyperSpec ? VNIR-A，Headwall Photonics Inc）、電控傳輸平臺(tái)、鹵素?zé)舻冉M成。高光譜成像儀作為高光譜成像系統(tǒng)核心部件，其攝像機(jī)為圖像傳感器（Charge Coupled Device，CDD）、光譜儀為可見(jiàn)/近紅外光譜儀（光譜范圍400～1 000 nm，光譜采樣間隔0.74 nm，光譜通道數(shù)810，光譜分辨率2～3 nm）。

高光譜系統(tǒng)開(kāi)機(jī)預(yù)熱30 min，保證照射光源穩(wěn)定。將樣本平鋪在移動(dòng)平臺(tái)，通過(guò)Hyperspec軟件平臺(tái)設(shè)置載物臺(tái)移動(dòng)速度為5 mm/s。為消除暗電流及光源分布不均勻?qū)Ω吖庾V成像造成影響，需要對(duì)樣本圖像進(jìn)行黑白矯正[23]，用以下公式可以獲得校正后的反射強(qiáng)度R：

式中R為豬肉未經(jīng)矯正的高光譜圖像，R為100%反射率條件下的白色標(biāo)定圖像，R為0%反射率條件下的全黑色標(biāo)定圖像，R為校正后的光譜反射強(qiáng)度。

1.4 光譜特征預(yù)處理

使用ENVI5.3軟件，提取每個(gè)樣本感興趣區(qū)域，并計(jì)算該區(qū)域內(nèi)豬肉像素平均反射率作為光譜特征。其中240個(gè)樣本，每個(gè)樣本采集5個(gè)點(diǎn)，共測(cè)得1 200試驗(yàn)點(diǎn)。每個(gè)樣本點(diǎn)的光譜維度為800，光譜信息矩陣存儲(chǔ)格式為1 200行800列。由于儀器精準(zhǔn)度與測(cè)量環(huán)境導(dǎo)致光譜數(shù)據(jù)產(chǎn)生偏差，為消除噪聲提高光譜分辨率，采取卷積平滑（Savitzky-Golay，SG）對(duì)光譜信號(hào)去噪，圖3反映預(yù)處理前后光譜特征變化，對(duì)比圖3a、3b發(fā)現(xiàn)：經(jīng)過(guò)SG預(yù)處理后高光譜曲線平滑度提高，毛刺減少。

1.5 高光譜圖像信息提取

樣本在每個(gè)波長(zhǎng)有一張圖像，共計(jì)800幅圖像，相鄰波長(zhǎng)圖像信息高度相關(guān)，不利于圖像信息提取與儲(chǔ)存[24]。使用主成分分析法（Principal Component Analysis，PCA）對(duì)高光譜圖像數(shù)據(jù)進(jìn)行降維（樣本選取的矩陣為：××，其中為光譜波段數(shù)，=800，為二維圖像的寬度，50 pixels，為二維圖像的高度，50 pixels），提取方差貢獻(xiàn)率大的主成分因子。使用ENVI5.3軟件將高光譜圖像經(jīng)過(guò)線性組合后形成主成分圖像。

前3個(gè)主成分圖像累計(jì)貢獻(xiàn)率達(dá)到90.62%。其中第一主成分貢獻(xiàn)率為88.50%，表達(dá)信息量最多，選取第一主成分圖像用于圖像信息提取。將第一主成分圖像尺寸統(tǒng)一為16 pixel×16 pixel，并展平為一維向量，每幅圖像中包含768個(gè)像素點(diǎn)。

1.6 卷積自編碼器

Hinton等[25]提出自編碼器（Autoencoder，AE）用于特征提取，Chen等[26]將多個(gè)AE采用級(jí)聯(lián)堆疊構(gòu)成SAE用于高光譜數(shù)據(jù)深層特征提取。CAE為SAE改進(jìn)形式，以端對(duì)端方式完成卷積與反卷積運(yùn)算，實(shí)現(xiàn)光譜與圖像信息深度特征提取。卷積自編碼器利用CNN模型稀疏連接和權(quán)值共享特性，解決SAE因?qū)訑?shù)增加參數(shù)成指數(shù)增長(zhǎng)問(wèn)題，減少模型參數(shù)避免算法過(guò)擬合[27-28]，提高特征提取效率。卷積自編碼器模型如圖4所示。該模型分為編碼器、解碼器兩部分，編碼器由各種卷積層與池化層組成，對(duì)輸入向量進(jìn)行編碼，提取向量深度特征，降低向量維度。解碼器主要由反卷積層與上采樣層構(gòu)成，用于特征數(shù)據(jù)重構(gòu)。

1.7 CNN回歸模型

卷積神經(jīng)網(wǎng)絡(luò)回歸模型是一種多層監(jiān)督學(xué)習(xí)的神經(jīng)網(wǎng)絡(luò)，包括輸入層、卷積層、池化層、輸出層，其基本結(jié)構(gòu)如圖5所示。卷積層與池化層是實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)特征提取功能核心模塊[29]，卷積層中通過(guò)卷積核對(duì)輸入特征矢量進(jìn)行卷積操作，再利用非線性激活函數(shù)構(gòu)建輸出特征矢量，其數(shù)學(xué)模型如式（8）所示：

最大池化層是對(duì)輸入數(shù)據(jù)縮放映射，在輸入中提取局部最大值，降低訓(xùn)練參數(shù)數(shù)量，提高特征魯棒性，其數(shù)學(xué)模型如式（9）所示：

1.8 PLSR與SVR回歸模型

PLSR集成主成分分析、多元線性回歸分析等優(yōu)點(diǎn)，在光譜信息存在多重相關(guān)性條件下建立回歸模型，該模型潛變量通過(guò)自變量與目標(biāo)變量間的協(xié)方差提高模型預(yù)測(cè)精度，是對(duì)光譜數(shù)據(jù)分析的一種多元統(tǒng)計(jì)分析方法。

SVR模型能解決有限樣本與數(shù)據(jù)非線性問(wèn)題，對(duì)高光譜數(shù)據(jù)分析有較大優(yōu)勢(shì)。該算法將樣本集從原始特征空間映射到高維特征空間，然后在高維空間中構(gòu)造線性決策函數(shù)來(lái)實(shí)現(xiàn)線性回歸，本文采用PLSR、SVR與CNN模型建立豬肉中肌紅蛋白無(wú)損檢測(cè)預(yù)測(cè)，通過(guò)對(duì)比3個(gè)模型決定系數(shù)與均方根誤差選擇較優(yōu)模型。

1.9 試驗(yàn)平臺(tái)

本文中模型的訓(xùn)練與測(cè)試所用電腦的主要配置為PC Intel(R) Core(TM) i5-4200H CPU @ 2.80GHz 2.79 GHz、操作系統(tǒng)為windows10。使用Keras深度學(xué)習(xí)框架，采用python3.7作為編程語(yǔ)言。

2 結(jié)果與分析

2.1 光譜深度特征建模結(jié)果與分析

預(yù)處理后的光譜信息經(jīng)過(guò)卷積編碼器特征提取后，每個(gè)樣本值的維度由800降到64。圖6a為某個(gè)樣本的原始光譜信息，經(jīng)過(guò)卷積編碼器后提取的深度特征結(jié)果如圖6b所示，深度特征經(jīng)過(guò)解碼器重構(gòu)后光譜信息如圖 6c所示。經(jīng)過(guò)對(duì)比發(fā)現(xiàn)，重構(gòu)后光譜信息變化趨勢(shì)與原始光譜信息大致相同。

針對(duì)不同放置時(shí)間的豬肉樣本，共獲得1 200個(gè)樣本值（訓(xùn)練集900個(gè)，預(yù)測(cè)集300個(gè)），分別對(duì)全光譜波段與光譜深度特征建立CNN預(yù)測(cè)模型。800個(gè)全光譜波段存儲(chǔ)矩陣格式為1 200×800（行×列），深度光譜特征的存儲(chǔ)矩陣格式為1 200×64?；谌ǘ闻c深度光譜特征的肌紅蛋白值含量CNN預(yù)測(cè)模型評(píng)價(jià)結(jié)果如表1所示。

表1 基于光譜特征的CNN模型預(yù)測(cè)結(jié)果

2.2 圖像特征信息建模結(jié)果與分析

通過(guò)ENVI5.3軟件得到某樣本豬肉第一主成分圖像如圖7a，將第一主成分圖像轉(zhuǎn)換為768維列向量如圖7b所示，將列向量作為CAE的輸入，提取主成分圖像的深度特征如圖7c，深度特征經(jīng)過(guò)重構(gòu)解碼，圖像信息如圖 7d所示。對(duì)比發(fā)現(xiàn)經(jīng)過(guò)CAE重構(gòu)的圖像信息與原始信息變化趨勢(shì)大致相同，可以得出卷積編碼器可用于對(duì)高光譜主成分圖像深度特征提取。

全圖像信息經(jīng)過(guò)卷積編碼器特征提取后得到深度圖像信息，維度由768降到64。分別對(duì)全圖像特征與深度圖像特征建立CNN預(yù)測(cè)模型，訓(xùn)練集與預(yù)測(cè)集的劃分方法與光譜信息建模相同，按照測(cè)試集與預(yù)測(cè)集3∶1的比例劃分?；谌繄D像特征與深度圖像特征建立CNN肌紅蛋白預(yù)測(cè)模型評(píng)價(jià)結(jié)果如表2所示。

表2 基于圖像特征的CNN模型預(yù)測(cè)結(jié)果

2.3 基于融合信息的建模結(jié)果與分析

參考文獻(xiàn)[12]數(shù)據(jù)融合方法，將光譜信息與圖像信息進(jìn)行數(shù)據(jù)層的融合，800維的光譜信息與768維的主成分圖像數(shù)據(jù)得到1 568維列向量，并輸入到CAE提取融合深度特征，樣本數(shù)據(jù)集的劃分與2.1節(jié)相同。為進(jìn)一步驗(yàn)證基于高光譜圖像信息預(yù)測(cè)豬肉肌紅蛋白含量的有效性，設(shè)計(jì)PLSR、SVR與CNN模型的肌紅蛋白對(duì)比試驗(yàn)，建模結(jié)果如表3。

表3 基于融合信息模型預(yù)測(cè)結(jié)果

基于以上結(jié)果分析在提取光譜特征時(shí)，采集區(qū)域集中在精瘦肉，采集的光譜信息與在采集較大光斑的圖像反射率不同，導(dǎo)致兩者的光譜曲線有差異，光譜信息包含特征點(diǎn)不足。通過(guò)提取樣本主成分圖像的圖像特征，來(lái)彌補(bǔ)不足。豬肉樣本高光譜主成分圖像包含樣本顏色、紋理等特征，卷積神經(jīng)網(wǎng)絡(luò)較強(qiáng)的特征提取能力，提取圖像深層次特征。采用圖像特征與光譜特征的融合能獲取更加全面的特征點(diǎn)。為進(jìn)一步驗(yàn)證模型的可靠性，再次隨機(jī)選取50個(gè)樣本數(shù)據(jù)作為預(yù)測(cè)集，肌紅蛋白含量預(yù)測(cè)值與實(shí)測(cè)值比較如圖8所示，預(yù)測(cè)集決定系數(shù)均大于0.85，進(jìn)一步驗(yàn)證模型具有較好的預(yù)測(cè)能力。

3 結(jié) 論

本文采集冷藏4 ℃的豬肉在0～5 d試驗(yàn)周期內(nèi)豬肉高光譜的光譜與圖像信息，采用卷積自編碼器對(duì)光譜信息、圖像信息及兩者融合信息進(jìn)行深度特征提取，并建立卷積神經(jīng)網(wǎng)絡(luò)（Convolutional Neural Network，CNN）、偏最小二乘回歸（Partial Least Squares Regression，PLSR）、持向量機(jī)回歸（Support Vector Regression，SVR）豬肉肌紅蛋白含量預(yù)測(cè)模型，得到以下結(jié)論：

1）基于全波段與經(jīng)過(guò)卷積自編碼器提取深度特征建立CNN肌紅蛋白預(yù)測(cè)模型，其中全波段光譜信息建立肌紅蛋白模型，脫氧肌紅蛋白（Deoxymyoglobin，DeoMb）、氧合肌紅蛋白（Oxygenated myoglobin，OxyMb）、高鐵肌紅蛋白（Metmyoglobin，MetMb）的預(yù)測(cè)集決定系數(shù)分別為0.855 1、0.886 2、0.861 8?；谏疃忍卣鹘⒒貧w模型DeoMb，OxyMb，MetMb的預(yù)測(cè)集決定系數(shù)分別0.923 8、0.920 3、0.909 2，基于深度光譜特征建立模型決定系數(shù)均有提高?？梢缘贸?，卷積神經(jīng)網(wǎng)絡(luò)對(duì)于光譜數(shù)據(jù)有特征提取功能，可用于光譜數(shù)據(jù)研究與分析。

2）基于光譜-圖像深度融合特征建立卷積神經(jīng)網(wǎng)絡(luò)肌紅蛋白回歸模型，DeoMb，OxyMb，MetMb的預(yù)測(cè)集決定系數(shù)分別0.964 5、0.973 2、0.958 5，相比于建立的光譜、圖像特征模型，其預(yù)測(cè)集決定系數(shù)較高，均方誤差較低。說(shuō)明融合特征包含更加全面的豬肉樣本信息，基于融合特征建立回歸模型能提高預(yù)測(cè)準(zhǔn)確度。

3）基于圖譜融合特征建立CNN、PLSR、SVR 3個(gè)回歸模型，對(duì)比三者決定系數(shù)可以得出：利用融合特征建立CNN預(yù)測(cè)模型準(zhǔn)確度較高，有廣闊應(yīng)用場(chǎng)景，為高光譜圖像處理提供新的方法。

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Non-destructive detection of pork myoglobin content based on hyperspectral technology

Wang Lishu, Hu Jinyao, Fang Junlong※, Chen Xi, Li Chuang

(,,150030)

Hyperspectral imaging system can widely be expected to acquire a set of sample images within certain spectral bands in each pixel at the same time. In this study, rapid detection was proposed for the myoglobin content in pork samples using spectral images and deep learning. The pork was placed under the cold storage conditions at 4°C, where a total of 250 pork samples were settled at different times (0-5 d). A hyperspectral imager was used to collect the pork hyperspectral images (400 to 1 000 nm). ENVI5.3 software was also selected to determine the region of interest (ROI) in the hyperspectral images, thereby extracting the full-band average spectrum and principal component image of ROI. Subsequently, a Savitzky-Golay (SG) filter was used to denoise the spectral information for the curve smoothness and spectral resolution. A convolutional auto encoder (CAE) was utilized to extract spectral depth features. A prediction model was finally established for the content of deoxymyolglobin (DeoMb), oxymyoglobin (OxyMb), and metmyoglobin (MetMb) in the pork samples. The results showed that the determination coefficients of test datasets were 0.923 8, 0.920 3, and 0.909 2, and the root mean square errors (RMSE) were 0.033 4, 0.619 7, and 0.809 1, respectively. Furthermore, the image information of adjacent wavelengths was highly correlated against the image extraction and storage. Principal Component Analysis (PCA) was utilized to reduce the dimension of hyperspectral images for better storage and processing. As such, the images under all bands were linearly combined to form a principal component image in the ENVI5.3 software. The first three principal component images represented 90.62% of the original hyperspectral image, where the contribution rate of the first principal component was 88.50%, indicating the most information. Therefore, the first principal component image was selected for the subsequent image extraction. The first principal component image was unified to the size of 16×16 pixels, and then converted into a 768-dimensional column vector for the extraction of image depth features using a convolutional encoder. DeoM, OxyMb, and MetMb content prediction models were established using image depth features, in which the determination coefficients of test datasets were 0.772 1, 0.828 7, and 0.825 4, while the RMSE of prediction were 0.105 8, 1.302 7, and 1.566 7. The spectral and image features were fused at the data level, and then the fusion data was input into the CAE to extract the deep fusion features. The DeoMb, OxyMb, and MetMb content prediction models were also established using the fusion depth features. The determination coefficients of test datasets were 0.964 5, 0.973 2, and 0.958 5, while the RMSE of prediction were 0.015 8, 0.226 6, and 0.381 6. Obviously, the determination coefficients of the test dataset were improved, while the RMSE were reduced, compared with the individual image and spectrum information. Partial least square regression (PLSR) and support vector machine regression (SVR) prediction models were also established to further verify the relationship between the graph-spectrum fusion feature and pork myoglobin. It was found that the determination coefficients of the test dataset were greater than 0.85. Consequently, the convolutional autoencoder can be expected to extract the deep fusion features of image and spectral information. Moreover, the fusion features can better reflect the internal and external information of pork. The CNN regression model using the fusion features can also be used to improve the prediction accuracy. This finding can provide a new better way to detect the myoglobin content in pork using hyperspectral imaging.

nondestructive detection; spectral feature; hyperspectral image; convolutional neural network; convolutional autoencoder

王立舒，胡金耀，房俊龍，等. 基于高光譜技術(shù)的豬肉肌紅蛋白含量無(wú)損檢測(cè)[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2021，37(16)：287-294.doi：10.11975/j.issn.1002-6819.2021.16.035 http://www.tcsae.org

Wang Lishu, Hu Jinyao, Fang Junlong, et al. Non-destructive detection of pork myoglobin content based on hyperspectral technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(16): 287-294. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2021.16.035 http://www.tcsae.org

2020-03-18

2021-08-13

黑龍江省教育廳科技課題（12521038）

王立舒，教授，博士，博導(dǎo)。研究方向：農(nóng)業(yè)電氣化與自動(dòng)化；電力新能源開(kāi)發(fā)與利用。Email：wanglishu@neau.edu.cn

房俊龍，教授，博士，博導(dǎo)。研究方向：電力系統(tǒng)自動(dòng)化、信息處理與智能測(cè)控。Email：junlongfang@126.com

10.11975/j.issn.1002-6819.2021.16.035

S126

A

1002-6819(2021)-16-0287-08