Volatile components of white Hypsizygus marmoreus detected by electronic nose and HS-SPME-GC-MS: Influence of four drying methods

Four drying methods, viz hot air drying (HD), vacuum drying (VD), microwave-vacuum drying (MVD), and vacuum freeze-drying (FD), were used to dry white Hypsizygus marmoreus (WHM). The volatile and taste components of the dried WHM were comparatively analysed by electronic nose technology (E-nose) and head-space solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GS-MS). Principal component analysis (PCA) showed that E-nose could distinguish fresh WHM and four dried products clearly. Dominant volatile components in fresh WHM were found to be 1-octen-3-ol, 3-octanone, and (E)-2-nonenal. However, it was found that the drying methods caused damage to the 8-carbon compounds (C8), especially to 8-carbon alcohol greatly. For instance, HD produced more ester materials, MVD yielded more aldehyde compounds, FD brought more hydrocarbon products, and VD produced more ketone substances.     

Smart storage technologies applied to fresh foods: A review

ABSTRACT

Fresh foods are perishable, seasonal and regional in nature and their storage, transportation, and preservation of freshness are quite challenging. Smart storage technologies can online detection and monitor the changes of quality parameters and storage environment of fresh foods during storage, so that operators can make timely adjustments to reduce the loss. This article reviews the smart storage technologies from two aspects: online detection technologies and smartly monitoring technologies for fresh foods. Online detection technologies include electronic nose, nuclear magnetic resonance (NMR), near infrared spectroscopy (NIRS), hyperspectral imaging and computer vision. Smartly monitoring technologies mainly include some intelligent indicators for monitoring the change of storage environment. Smart storage technologies applied to fresh foods need to be highly efficient and nondestructive and need to be competitively priced. In this work, we have critically reviewed the principles, applications, and development trends of smart storage technologies.     

Seasonal variation in the odour characteristics of whole milk powder

Seasonal changes in whole milk powder odour characteristics were monitored using SPME-GC and E-nose methodologies. ANOVA showed a significant effect of the season on dimethyl sulphide, n-pentanal, n-hexanal, and butyric acid. Whole milk powder manufactured in summer season had significantly (p < 0.05) higher levels of n-hexanal, n-pentanal and dimethyl sulphide as compared to autumn and winter seasons. On the other hand, butyric acid showed significant differences (p < 0.05) among autumn and spring. Applying linear discriminant analysis, seasonal variations in the odour profiles assessed by electronic nose were observed. A better classification outcome was obtained when volatile compounds and electronic nose data were analysed together, obtaining a success rate of 89.5% and 70.7% of the original cases and after cross-validation respectively. As in other application reported in the literature, E-nose approach represents an alternative technique to traditional methods of odour measurement.     

利用电子鼻区分羊奶中不同浓度抗生素

采用电子鼻方法,利用主成分分析(PCA)和线性判别分析(LDA)进行数据采集,对添加硫酸土霉素、盐酸链霉素、硫酸庆大霉素的羊奶进行快速检测与识别.结果表明,PCA只能区分盐酸链霉素样品,LDA的区分效果明显优于PCA.电子鼻能够有效的区分添加以上3种不同浓度抗生素的羊奶.     

无损检测光动力杀菌鲜切苹果气味及糖度

目的利用电子鼻及高光谱成像无损检测技术评价光动力杀菌对鲜切苹果气味及糖度的影响。方法通过电子鼻,基于雷达图、负荷加载(loadings)和主成分分析(principal components analysis,PCA)评价光动力处理鲜切苹果风味变化。卷积平滑(Savitzkye-Golay,S-G)和多元散射矫正(multiplicative signal correction,MSC)预处理高光谱数据,以偏最小二乘法建立糖度数学模型。结果鲜切苹果贮藏8 d后挥发性物质增加,其中2、8号传感器在区分其香气成分中起关键作用,结果显示光动力处理对鲜切苹果气味影响较小。高光谱S-G模型拟合效果较好,预测均方根误差为0.5320,相关系数r为0.8452,且光动力处理有助于维持其贮藏前期可溶性糖分含量。结论电子鼻及高光谱技术可以无损检测鲜切果蔬品质,可为光动力处理作为新型非热杀菌技术在鲜切果蔬保鲜的应用提供理论参考。     

基于顶空气相色谱-离子迁移谱与电子鼻技术快速检测宁夏滩羊肉中掺假鸭肉

为实现掺假宁夏滩羊肉的准确快速检测,按照不同比例将鸭肉混入滩羊肉中制备掺假肉。基于顶空气相色谱-离子迁移谱（gas chromatography-ion mobility spectrometry,GC-IMS）及电子鼻结合统计学方法对掺假肉样进行快速检测。通过GC-IMS采集掺假肉气味图谱,利用主成分分析和相似度差异分析对掺假肉进行分析;采用线性判别法分析电子鼻传感器响应值,后采用多元线性回归建立电子鼻响应值与鸭肉掺假比例的定量模型。结果表明：通过GC-IMS气味图谱可直观看出掺假不同比例鸭肉的滩羊肉中挥发性有机物差异,但对于掺假比例相近的样品区分度不明显;采用线性判别分析可很好区分掺假不同比例鸭肉的滩羊肉;采用多元线性回归分析,以鸭肉掺假比例及电子鼻传感器响应值进行拟合,得到回归方程决定系数为R2=0.967 1,拟合度高,实际测定值与预测值具有较好的相关性。GC-IMS技术、电子鼻结合统计学方法可以对掺入不同比例鸭肉的滩羊肉进行鉴别。     

电子鼻结合顶空固相微萃取-气相色谱-质谱联用技术对干制乌贼挥发性成分分析

以乌贼为研究对象,采用电子鼻技术,结合顶空固相微萃取-气相色谱-质谱（headspace-solid phasemicroextraction-gas chromatography-mass,HS-SPME-GC-MS）联用技术,分析晾晒和热风烘干处理对乌贼挥发性风味物质的影响。结果表明,电子鼻分析结果能够很好地区分新鲜、晾晒和热风烘干3 种乌贼样品的风味。采用主成分分析、负荷加载分析以及线性判别分析可以量化主成分贡献率、传感器识别效应和样品间的风味区分度。HS-SPME-GC-MS分析结果表明,3 种样品共检出116 种不同的挥发性风味物质。其中新鲜乌贼为52 种,晾晒乌贼为64 种,热风烘干乌贼为71 种。新鲜乌贼经干制加工后,风味物质变化明显,产生新的醛类、烃类以及吡嗪类物质,而胺类物质的相对含量相对减少。吡嗪类物质在热风烘干乌贼中相对含量很高,是构成其特有的挥发性风味的主要贡献物质。     

Application of Gas Chromatography-Based Electronic Nose for Classification of Chinese Rice Wine by Wine Age

The potential of a gas chromatography (GC)-based electronic nose (E-nose) combined with chemometrics to classify Chinese rice wine by wine age was investigated. Olfactive fingerprints of 1-, 3-, and 5-year Chinese rice wine samples collected by the E-nose were analyzed by principal component analysis (PCA) and discriminant analysis (DA). Gas chromatography/mass spectrometry (GC/MS) was used for wine age validation purpose. The results indicated that the percentage of samples correctly classified by the E-nose was 96.88 %. It was concluded that the GC-based E-nose together with DA was a reliable method for wine age discrimination.     

电子鼻技术在果蔬病害检测中的研究进展

归纳和总结了近年来电子鼻技术在果蔬采后单种和多种病原菌侵染检测中的最新研究成果,分析了当前电子鼻技术存在的缺点,预测了电子鼻技术在果蔬病害检测中的发展方向。     

A selection method for feature vectors of electronic nose signal based on wilks Λ–statistic

There are mainly two selection methods for different features of electronic nose (E-nose) which is used to identify different samples, namely visual inspection and correct rate of discrimination result. The visual inspection is not a quantitative method. Besides, when the correct rates of discrimination result are identical for different features, the identification difference of different features is not evaluated accurately and quantitatively. To get a better feature vector for identifying different samples, a selection method was studied in-depth in which Wilks Λ–statistic was employed as a selection index for different features. At the same time, three different kinds of Chinese vinegar and three of Chinese milk were taken and tested by an E-nose. Five different features were extracted from the E-nose signals which are variance value (VARV), integral value (INV), mean value of relative steady-state responses (MVRSR), mean-differential coefficient value (MDCV) and energy value of wavelet packet decomposition (WE). The best feature vectors of these five features were obtained using the selection method and its effectiveness was respectively proved by the visual inspection and Fisher discriminant analysis (FDA) correct rate of vinegar and milk samples.     

Rapid freshness analysis of mantis shrimps (<Emphasis Type="Italic">Oratosquilla oratoria</Emphasis>) by using electronic nose

In this paper, rapid freshness analysis method of mantis shrimps (MSs) (Oratosquilla oratoria) by using electronic nose (e-nose) was investigated. Shrimps were stored at 4° and ?20°. E-nose responses to samples were measured. Meanwhile, appearance of MS was recorded. Total volatile basic nitrogen (TVB-N) index was examined to provide a standard freshness indicator for samples according to Chinese national standard protocol. E-nose measurement data was processed by principal component analysis (PCA) and non-linear bistable stochastic resonance (SR). Experimental results demonstrated that e-nose sensitively responded to shrimps. PCA results failed to discriminate all shrimps. SR signal-to-noise ratio (SNR) spectrum successfully discriminated all shrimp samples stored at 4° and ?20°. E-nose MS freshness degree forecasting models were developed using SNR maximums. Combining TVB-N examination results, the forecasting accuracy of developed freshness analysis model is 91.67 %. The proposed method had some advantages including rapid analysis, easy operating, non-destructive, low cost, etc.     

Electronic nose system fabrication and application in large yellow croaker (<Emphasis Type="Italic">Pseudosciaena crocea</Emphasis>) fressness prediction

In this paper, an electronic nose (E-nose) system was fabricated, and its application in large yellow croaker (Pseudosciaena crocea) freshness prediction was also explored. E-nose responses to samples stored at 277 K were measured for 8 days. Freshness indexes, such as total viable counts (TVC), total volatile basic nitrogen (TVB-N) and K value, were synchronously examined by chemical examinations. Principal component analysis (PCA) and stochastic resonance (SR) were utilized for e-nose data analysis. Results suggested that PCA showed poor freshness discrimination result. SR signal-to-noise (SNR) spectrum using maximal SNR (\(Max_{SNR}\)) values quantitatively characterized freshness of all croakers. Multiple variable regression (MVR) result demonstrated that there was good linearity relationship between SR \(Max_{SNR}\) values and fish freshness indexes. Large yellow croaker freshness predicting model was developed by non-linear fitting regression on \(Max_{SNR}\) values with high accuracy and repeatability. Therefore, the method proposed in this paper provides a rapid and nondestructive methodology for freshness prediction of large yellow croakers. The predicting error of the developed model is 10 %.     

Paddy and maize moldy status characterization using electronic nose

Paddy and maize moldy status rapid detection using electronic nose (E-nose) was investigated in this paper. An E-nose system with eight metal oxide semiconductor gas sensors was used to measure moldy status of paddy and maize samples at room temperature. Principal component analysis (PCA) and stochastic resonance (SR) are used for data processing. PCA results showed that E-nose could distinguish paddy and maize samples in different moldy status. Signal-to-noise ratio (SNR) spectrum calculated by SR discriminated paddy and maize samples. Moldy status predicting models were developed based on SNR maximums linear fitting regression. Validating experiments demonstrated that the developed models presented high detecting accuracy. The proposed method is promising in grain quality rapid analysis applications.     

Detection of age and insect damage incurred by wheat, with an electronic nose

Wheats of five storage ages and with 15 degrees of insect damage were evaluated and classified by the static-headspace sampling method using an electronic nose (E-nose). A commercial E-nose (PEN2) comprising 10 metal-oxide semiconductor (MOS) sensors was used to generate a typical chemical fingerprint of the volatile compounds present in the samples. Principal-component analysis (PCA) and linear-discriminant analysis (LDA) were applied to the generated patterns to achieve classification into the five groups of different storage-age wheats and the 15 groups of different degrees of insect-damaged wheat. The results obtained indicated that the E-nose could discriminate successfully among wheats of different age and with different degrees of insect damage.     

Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system

Warehouse pests reduce the quantity, quality, and health of storage products. These parameters are protected by detecting and controlling related pests. Ephestia kuehniella (E. kuehniella) causes intense damage to the storage products, such as flour, almond, date and cereals. Thus, diagnosing pest densities for preventing and monitoring them by online alarming systems is important. The present study was designed to detect pest densities in white flour. For this purpose, an electronic nose (E-nose) system was applied by MOS sensors for pest density detection. PCA/LDA multivariate statistical models were built and relevant performances were compared for different instars of larvae. LDA provided the highest prediction abilities on the fifth instar with an accuracy of 90%. According to results, this system had the capability to differentiate between the pests densities, and thus the detection accuracy reflected the ability of the E-nose system for such purposes.     

电子鼻对两种食源性致病菌在菌株水平上区分的研究

利用基于金属氧化物传感器阵列的电子鼻技术对五株单增李斯特菌和五株副溶血性弧菌的挥发性代谢产物进行研究,结合主成分分析（PCA）和聚类分析（CA）等化学计量学方法对电子鼻原始数据进行统计学分析。结果显示,电子鼻模式识别技术能够很好地将五株单增李斯特菌的挥发性代谢产物图谱进行区分,而在同一技术条件下对五株副溶血性弧菌的挥发性代谢产物图谱只能进行部分区分,利用向这五株副溶血性弧菌的培养液中添加NaCl至饱和的方法,可以将五株菌完全区分开来,区分效果显著增强。实验表明利用电子鼻结合主成分分析和聚类分析技术对食源性致病菌在菌株水平上的区分和鉴定具有一定的可行性,有望将该技术开发成为一种快速、简便、无损的食源性致病菌新型检测分型方法。     

应用电子鼻对奶粉掺假快速检测的研究

利用由十个金属氧化物传感器组成的电子鼻对几种不同品牌的奶粉和掺入其他物质的奶粉样品分别进行了测量,并通过PCA和LDA法对数据进行了统计分析。数据分析显示所建立的奶粉电子鼻模型能够用于识别不同品牌的奶粉样品,同时电子鼻也能够鉴别奶粉是否有其他物质的掺入。另外基于电子鼻的灵敏性,其对于奶粉中掺入物的浓度也有一定的鉴定能力。研究结果表明电子鼻技术应用在奶粉掺假的快速检测上是可行的。     

基于电子鼻和GC-MS评价不同品种葡萄采后品质和挥发性物质的变化

为明确不同品种葡萄果实采后品质和挥发性物质的差异,本文通过顶空固相微萃取及气相-质谱联用技术(HS-SPME-GC-MS)测定4个不同品种葡萄新鲜果实挥发性物质成分和相对含量,并以电子鼻辅助验证,对其品质特性进行分析与探讨。结果表明:4个品种葡萄成熟果实的品质指标品种间差异较大。无核寒香蜜品种果实的可溶性固形物含量和可滴定酸含量较高,香悦维生素C含量高,硬度大。4个品种葡萄成熟果实中共检测到87种挥发性物质,主要成分为醇类、酯类和醛类。不同品种间主要挥发性物质存在差异。香悦、玫瑰香、金手指和无核寒香蜜葡萄品种果实中挥发性物质相对含量最高的物质分别为乙酸乙酯、沉香醇、青叶醛和乙酸乙酯,呈现出的风味分别为草莓香味、浓郁玫瑰香味、新鲜绿叶香味和草莓香味。利用电子鼻对不同品种葡萄果实挥发性物质进行主成分分析和线性判别分析,可以将4个品种葡萄完全区分开。因此,本试验明确了不同品种葡萄果实采后品质的差异,电子鼻结合GC-MS方法可以有效评价其挥发性物质差异。     

电子鼻结合顶空SPME-GC-MS联用技术分析贵州不同品种辣椒发酵后挥发性成分

以贵州5种不同辣椒品种为研究对象,采用电子鼻技术,结合顶空固相微萃取-气相色谱-质谱（solid phase microextraction-gas chromatography-mass,SPME-GC-MS）联用技术,对其发酵后挥发性成分进行分析。结果表明,电子鼻分析结果能够很好地区分不同辣椒品种发酵后的风味;采用主成分分析（principal component analysis,PCA）以及线性判别分析（linear discriminant analysis,LDA）可知,PCA和LDA主成分贡献率总和分别为99.93%、99.19%,均大于95%,说明传感器识别效应和样品间的风味区分度较好。SPME-GC-MS分析结果表明,5种样品共检出124种不同的挥发性风味物质。其中黄平线椒占97种,以酯类物质相对含量最高,为36.82%;施秉线椒和大方皱椒分别占88种和94种,且均以醇类物质相对含量最高,分别为31.88%、28.99%;百宜平面椒和花溪党武辣椒分别占89种和71种,均以烃类物质相对含量最高,分别为26.75%、35.08%。此结果与电子鼻PCA和LDA结果相一致。因此,通过SPME-GC-MS和电子鼻分析结果可知,电子鼻能够很好地区分贵州名优产地不同辣椒品种发酵后的风味。     

Spoiling and formaldehyde-containing detections in octopus with an E-nose

The seafood dipped with formaldehyde to prevent from spoiling by dishonest mongers is a big danger to the physical health of consumer. An E-nose with six TGS gas sensors was used for spoiling and formaldehyde-containing detection of seafood in this paper. Two static features R₀ (resistance in the air), S (sensor response), and one dynamic feature DR (desorption rate) were extracted. Fresh octopus samples dipped in water solutions with different formaldehyde concentrations were measured. In these measurements, the stability of sensors and features was evaluated and compared. The mean relative errors of these three features were 23.6%, 19.7%, and 4.1%, respectively. The results showed that the dynamic feature was more stable. With principal component analysis, the spoilage of seafood could be easily detected. And the correct recognition rate of different octopus samples was 93.1%. The results showed that electronic nose analysis could be an efficient method for seafood quality assessment.