基于機器視覺和工藝參數(shù)的針芽形綠茶外形品質(zhì)評價
發(fā)布時間：2019-01-04 15:47
【摘要】：外形是針芽形綠茶的關(guān)鍵感官評價指標,通常依據(jù)色澤、條形、嫩度和勻整度等表象特征進行人工評審,難以做到精準、客觀和量化評價。本文以自動化生產(chǎn)線機制的針芽形綠茶為研究對象,基于茶葉品質(zhì)、形成工藝和視覺形態(tài)等內(nèi)外因素,構(gòu)建了外形品質(zhì)的智能感官評價方法。首先,在線采集在制品的17個機制工藝參數(shù)和成品茶的圖像,進行圖像特征提取,選取9個顏色特征和6個紋理特征。進而,通過與專家感官評分進行關(guān)聯(lián)分析,明確了與感官品質(zhì)顯著相關(guān)的特征變量。為獲取高效的評價模型,采用偏最小二乘法(PLS)、極限學(xué)習(xí)機(ELM)和強預(yù)測器集成算法(ELM-Ada Boost)3種多元校正方法,分別建立了基于工藝或圖像特征的針芽形綠茶外形感官的量化評價模型。建模結(jié)果表明,基于圖像特征建立的ELM-Ada Boost模型(Rp=0.892,RPD大于2),其預(yù)測性能優(yōu)于其他模型,且具有更小的RMSEP(0.874)、Bias(-0.148)、SEP(0.226)和CV(0.018)值。同時,非線性模型的預(yù)測性能均高于PLS線性模型,能更好地表征工藝參數(shù)、圖像信息與感官評分之間的解析關(guān)系,且建模速度更快(0.014~0.281 s)。而Ada Boost法作為一種混合迭代算法,能進一步提升ELM模型的精度和泛化能力。結(jié)果表明,基于機器視覺和工藝評價針芽形綠茶外形品質(zhì)是可行的,為拓展茶葉感官品質(zhì)評價方法和專家工藝決策支持系統(tǒng)研制,提供理論依據(jù)和數(shù)據(jù)支撐。
[Abstract]:Shape is the key sensory evaluation index of needle bud green tea. It is difficult to evaluate accurately objectively and quantitatively according to the color strip tenderness and evenness of green tea. Based on the internal and external factors such as tea quality, forming process and visual form, the intelligent sensory evaluation method for the appearance quality of needle bud green tea with automatic production line mechanism was established in this paper. Firstly, 17 process parameters of in-process products and images of tea products were collected online, and 9 color features and 6 texture features were selected for image feature extraction. Furthermore, through the correlation analysis with the expert sensory score, the characteristic variables which are significantly related to the sensory quality are identified. In order to obtain an efficient evaluation model, three multivariate correction methods, partial least square (PLS),) extreme learning machine (ELM) and strong predictor integrated algorithm (ELM-Ada Boost), are adopted. A quantitative sensory evaluation model of needle bud green tea was established based on process or image features. The modeling results show that the prediction performance of the ELM-Ada Boost model (Rp=0.892,RPD > 2) based on image features is better than that of other models, and it has a smaller RMSEP (0.874), Bias (- 0.148). SEP (0.226) and CV (0.018) values. At the same time, the prediction performance of the nonlinear model is better than that of the PLS linear model, which can better represent the analytical relationship between the process parameters, the image information and the sensory score, and the modeling speed is faster (0.014 / 0.281 s). As a hybrid iterative algorithm, Ada Boost method can further improve the accuracy and generalization ability of ELM model. The results showed that it was feasible to evaluate the shape quality of needle bud green tea based on machine vision and technology, which provided theoretical basis and data support for the development of tea sensory quality evaluation method and expert process decision support system.
【作者單位】： 江蘇大學(xué)食品科學(xué)與食品工程學(xué)院;中國農(nóng)業(yè)科學(xué)院茶葉研究所;哥本哈根大學(xué)食品科學(xué)系;武義縣農(nóng)業(yè)局;
【基金】：國家自然科學(xué)基金項目(31271875) 浙江省自然科學(xué)基金項目(Y16C160009) 中央級公益性科研院所基本科研業(yè)務(wù)費專項(1610212016018)
【分類號】：TP391.41;TS272.51
本文編號：2400486
[Abstract]:Shape is the key sensory evaluation index of needle bud green tea. It is difficult to evaluate accurately objectively and quantitatively according to the color strip tenderness and evenness of green tea. Based on the internal and external factors such as tea quality, forming process and visual form, the intelligent sensory evaluation method for the appearance quality of needle bud green tea with automatic production line mechanism was established in this paper. Firstly, 17 process parameters of in-process products and images of tea products were collected online, and 9 color features and 6 texture features were selected for image feature extraction. Furthermore, through the correlation analysis with the expert sensory score, the characteristic variables which are significantly related to the sensory quality are identified. In order to obtain an efficient evaluation model, three multivariate correction methods, partial least square (PLS),) extreme learning machine (ELM) and strong predictor integrated algorithm (ELM-Ada Boost), are adopted. A quantitative sensory evaluation model of needle bud green tea was established based on process or image features. The modeling results show that the prediction performance of the ELM-Ada Boost model (Rp=0.892,RPD > 2) based on image features is better than that of other models, and it has a smaller RMSEP (0.874), Bias (- 0.148). SEP (0.226) and CV (0.018) values. At the same time, the prediction performance of the nonlinear model is better than that of the PLS linear model, which can better represent the analytical relationship between the process parameters, the image information and the sensory score, and the modeling speed is faster (0.014 / 0.281 s). As a hybrid iterative algorithm, Ada Boost method can further improve the accuracy and generalization ability of ELM model. The results showed that it was feasible to evaluate the shape quality of needle bud green tea based on machine vision and technology, which provided theoretical basis and data support for the development of tea sensory quality evaluation method and expert process decision support system.
【作者單位】： 江蘇大學(xué)食品科學(xué)與食品工程學(xué)院;中國農(nóng)業(yè)科學(xué)院茶葉研究所;哥本哈根大學(xué)食品科學(xué)系;武義縣農(nóng)業(yè)局;
【基金】：國家自然科學(xué)基金項目(31271875) 浙江省自然科學(xué)基金項目(Y16C160009) 中央級公益性科研院所基本科研業(yè)務(wù)費專項(1610212016018)
【分類號】：TP391.41;TS272.51
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