干燥过程中玉米水分质量分数均匀度的高光谱图像无损检测

水分含量均匀度是干燥过程一个重要指标,它是评价干燥食品质量和干燥工艺一个重要参数。作者以干燥过程中的玉米为研究对象,研究高光谱图像技术检测水分质量分数均匀度的方法。采用均值特征和标准差特征结合偏最小二乘法(PLS)建立预测模型;并用正交信号校正法对均值特征和标准差进行预处理。结果表明:均值特征和标准差进行预处理后所建立的模型效果较好,预测相关系数为0.839,预测均方根误差为1.74%,潜在变量的数目为2个。研究表明:高光谱图像技术可用于水分质量分数均匀度的直接无损检测。     

基于高光谱成像技术预测牡蛎干制加工过程中的水分含量

提出一种应用高光谱成像技术结合化学计量学检测牡蛎干制加工过程中水分含量的方法。采用高光谱成像系统,在400～1 100 nm范围内,采集到5个干燥时期的100个牡蛎干样本高光谱图像。提取所有样本感兴趣区域的平均光谱数据,对原始光谱数据进行多元散射校正(MSC)、卷积平滑(S-G)预处理,采用相关系数法提取8个特征波长。基于所提取的特征波长,建立光谱数据与水分含量的多元线形回归(MLR)和BP神经网络模型。结果表明:两种模型均有较好的预测效果。MLR模型的校正集、预测集和交叉验证集的相关系数较BP神经网络低;校正集、预测集和交叉验证集均方根误差分析结果表明,BP神经网络效果较MLR好。高光谱成像技术结合化学计量学方法可检测牡蛎干制过程中水分含量的变化。     

冷鲜羊肉冷藏时间和水分含量的高光谱无损检测

利用可见-近红外高光谱成像技术对冷鲜羊肉的冷藏时间和水分含量进行无损检测。通过波长400～1 000 nm可见-近红外高光谱系统采集160 个羊肉样本光谱信息,优选主成分-14-线性判别法对原始光谱建立羊肉冷藏时间的判别模型,校正集对羊肉冷藏时间的判别率为99.17%,预测集为100%,模型可较好地判别羊肉的冷藏时间。其次,针对羊肉冷藏过程中水分含量的变化,优选最佳预处理方法并运用偏最小二乘回归（partial leastsquares regression,PLSR）法建立水分含量预测模型;结果表明,经过Savitzky-Golay卷积平滑预处理的PLSR模型对水分含量的建模效果最优,校正集和预测集相关系数分别为0.888和0.784,交互验证均方根误差为0.696。研究表明,采用可见-近红外高光谱成像技术对冷鲜羊肉冷藏时间的判别和冷藏过程中羊肉水分含量的快速预测是可行的。     

高光谱成像技术定量可视化检测熟牛肉中挥发性盐基氮的含量

为了能够快速、准确的检测出熟牛肉在冷藏过程中的新鲜状况,尝试利用高光谱成像技术对熟牛肉中的挥发性盐基氮(TVB-N)含量进行定量可视化分析。采集400~1000 nm范围内样品高光谱图像,采用变量组合集群分析法(VCPA)提取出6个光谱特征波段变量,针对特征波段图像,利用Tamura算法共提取出18个纹理特征变量,基于RGB颜色模型,分别计算出R、G和B分量图中共9个颜色特征变量。利用粒子群优化最小二乘支持向量机(PSO-LS-SVM)算法分别建立了不同变量组合的TVB-N含量预测模型。经分析比较,基于光谱与颜色特征融合的PSO-LS-SVM模型展现出最优的预测能力,预测集决定系数(R2p)和均方根误差(RMSEP)分别为0.955和1.093。利用最优模型将TVB-N含量进行可视化表达。结果表明,融合高光谱图像中光谱与颜色特征并结合PSO-LS-SVM算法对熟牛肉中TVB-N含量进行准确的预测与可视化表达是可行的,该研究可为其它肉及肉制品新鲜度检测提供理论参考。     

微波加热技术在小麦水分测量中的应用

本文介绍了采用微波加热法测量小麦水分的原理,研究了PIC微波控制器对微波的量和微波反射板的调节控制,通过试验分析了微波加热与小麦水分挥发的关系,并在上述分析研究的基础上给出微波加热测量豫麦水分的具体分析实例.     

Potential of Hyperspectral Imaging for Rapid Prediction of Anthocyanin Content of Purple-Fleshed Sweet Potato Slices During Drying Process

This study aimed to investigate the potential of hyperspectral imaging technique in tandem with chemometrics analysis for rapid and nondestructive determination of anthocyanin content within purple-fleshed sweet potato (PFSP) during drying process. Hyperspectral images of PFSP in the spectral range of 371–1023 nm were obtained during contact ultrasound-assisted hot air drying (CUHAD) process, and the reference anthocyanin contents of PFSP were measured by a traditional method. Partial least square regression (PLSR) and least-square support vector machine (LS-SVM) were applied to establish the calibration models based on raw extracted spectrum and spectrum preprocessed by four different methods. In order to simplify the calibration model, three algorithms including PLSR, LS-SVM, and multiple linear regression (MLR) were used to build models based on ten optimal wavelengths selected by regression coefficients (RC) method. The results showed that the RC-MLR yielded best results with the coefficient of determination for calibration (\( {R}_C^2 \)) of 0.868 and coefficient of determination for prediction (\( {R}_P^2 \)) of 0.866. Finally, distribution maps were developed based on an image processing algorithm to visualize anthocyanin content of PFSP at different drying periods which cannot be achieved by conventional methods. The overall results demonstrated that hyperspectral imaging technique is a useful tool for rapid and nondestructive determination of the anthocyanin content during drying process.     

Inactivation of Soybean Lipoxygenase by Microwave Heating: Effect of Moisture Content and Exposure Time

A microwave oven (2450 MHz) was used as heating source to inactivate soybean lipoxygenase. Variables studied were initial moisture level of the soybean and time of heating. After exposure to microwave, the temperature, the moisture loss, the residual lipoxygcnase activity and the nitrogen solubility index (NSI) of the soybeans were determined. No linear relationship was observed between lipoxygenase activity and NSI. Complete inactivation of lipoxygenase in soybeans with moisture content between 26.9 and 56.8% was reached after 210 sec of microwave heating, corresponding to NSI between 49.4 and 39.2%, respectively. The beans with their innate moisture (8.7%) had approximately 98% inactivation of lipoxygenase and 56.1% of NSI after 240 sec of exposure.     

过程稳态分析在加料工序含水率控制中的应用

为科学评价烟草加工过程的工序能力,根据过程稳态的基本特征,以加料工序含水率控制为例,对烟草加工的全过程进行了系统的数理分析。结果表明:利用数理统计方法,剔除非稳态质量数据对SPC的影响,可对加工过程的稳态进行判定,准确评价加料工序的含水率控制水平,对稳态条件下的过程能力进行定性和定量的描述及评价。     

Smart NMR Method of Measurement of Moisture Content of Vegetables During Microwave Vacuum Drying

Microwave drying is usually combined with vacuum environment in conjunction with hot air flow to draw the moisture rapidly. The moisture content of the vegetables undergoing drying is hard to measure online. This research designed a microwave vacuum drying (MVD)-low-field nuclear magnetic resonance (NMR) smart device and investigated the feasibility of NMR method for online measurement of state of moisture during MVD. The relation between the signal amplitude (A ₂) and the true moisture content (M ₁) of six kinds of vegetables (mushroom, carrot, potato, lotus, edamame, vegetable corn) was fitted to estimate if NMR can measure the M ₁ of vegetables directly. Results showed that A ₂ and M ₁ of different fresh vegetables had no single empirical mathematical model to fit. However, for each kind of these vegetables, the A ₂ and corresponding M ₁ in different MVD stages showed a significant linear relationship. The predicted moisture content (M ₂) of mushroom: M ₂ = 5.25351 × 10^?4 A ₂ ? 0.34042, R = 0.996; carrot: M ₂ = 5.78756 × 10^?4 A ₂ ? 0.14108, R = 0.998; potato: M ₂ = 3.10019 × 10^?4 A ₂ ? 0.10612, R = 0.991; lotus: M ₂ = 2.32415 × 10^?4 A ₂ ? 0.01573, R = 0.998; edamame: M ₂ = 3.13310 × 10^?4 A ₂ ? 0.4198, R = 0.996; vegetable corn: M ₂ = 1.69461 × 10^?4 A ₂ ? 0.09063, R = 0.995. The linear models between M ₂ and A ₂ were able to estimate the end point (M ₁ < 8%) of MVD with a high accuracy (P > 0.950).     

Microwave Bumping: Quantifying Explosions in Foods During Microwave Heating

An apparatus was developed to quantify the degree of bumping due to the explosion of food particulates during microwave heating. The apparatus consisted of a sound recording device, a digital storage oscilloscope, and an electric filter. Degree of bumping for a product was defined by integration of the magnitude of an explosion over time of bumping. The sound spectrum of explosion was analyzed by Fast Fourier Transform (FFT) analysis. Frequency of bumping was usually in the range 1.6 kHz to 8 kHz. The character of microwave bumping varied according to localized microwave superheating effects, product formulations, and prior heat treatment of the food.     

微波加热法测定盐产品中的水分

文章介绍了用微波加热法快速测定盐业产品中的水分含量,并通过实验说明微波加热法与传统的烘失重法所测得的结果是一致的,且具有简便快速的特点,在实际工作中具有很强的实用性。     

高光谱成像技术检测延边黄牛肉的新鲜度

为了提高黄牛肉的质量以及食用安全性,本研究采用高光谱成像技术对延边黄牛肉新鲜度进行检测。通过成像系统采集黄牛肉样品的高光谱图像,对高光谱图像做黑白校正处理,结合S-G滤波和均值滤波对处理后的高光谱图像进行去噪处理,并采用主成分分析法对高光谱数据进行降维。采用半微量定氮法测定延边黄牛肉的挥发性盐基氮含量,通过多个BP-ANN弱分类器组建强分类器,将延边黄牛肉的挥发性盐基氮含量输入强分类器中,以挥发性盐基氮含量为指标,根据分类结果实现延边黄牛肉新鲜度的检测。实验结果表明,当波长为700~750 nm时,延边黄牛肉的挥发性盐基氮含量最高,表明肉内部被破坏的程度较高,肉类的新鲜度越差;在第10~11 d时,延边黄牛肉TVB-N质量分数变化较为明显;采用所提方法对高光谱图像去噪后,信号频率由-15 d B^15 d B区间波动变为-5 dB^5 dB区间波动,信号的频率波动较小,趋于稳定,表明所提方法可有效的去除图像中存在的噪声;所提方法的检测结果准确率最高可达99%,具有较高的检测准确率,且去噪效果较好。     

基于高光谱和超声成像技术的原切与合成调理牛排鉴别

针对市场上存在合成调理牛排冒充原切售卖的现象,研究利用高光谱和超声成像技术对它们进行鉴别的方法。分别采集原切与合成调理牛排的高光谱和超声图像信息,利用灰度共生矩阵法提取图像的纹理特征值,分别建立线性判别分析、K最邻近（K-nearest neighbor,KNN）、反向传播人工神经网络和极限学习机（extreme learning machine,ELM）4?种鉴别模型,而后将2?种技术数据融合建模,并采用连续投影法、竞争性自适应重加权算法（competitive adaptive reweighted sampling,CARS）、变量组合集群分析（variables combination population analysis,VCPA）法3?种方法筛选特征变量建模。结果表明：合成调理牛排的肉块组织均匀,超声图像信号弱、均一性好,与原切调理牛排图像存在差异。高光谱和超声成像技术的最佳模型分别为KNN和ELM,模型预测集识别率分别为95.00%和90.00%。数据融合后建模,最佳模型ELM模型预测集识别率模型为97.50%,在3?种变量选择方法中,CARS和VCPA选择的纹理变量建立的模型预测集识别率达到100.00%。研究表明高光谱和超声成像数据融合结合变量选择方法可以快速准确地鉴别原切和合成调理牛排。     

微波加热法测定调味料中的水分

采用微波加热法测定调味料中的水分，通过正交设计实验确定了测定条件，微波加热时间4-5min，样品质量3－4g，微波输出功率720W。对同一样品平行测定7次，S＝0。37，CV＝0．036，与蒸馏法比较，无明显差异。     

基于高光谱的单粒小麦籽水分分布预测

为预测单粒小麦籽的含水量,将小麦籽分成折痕向上(记为A面)和折痕向下(记为B面)进行高光谱扫描,建立4种预测模型,记为MA、MB、MC和MD。其中,模型MA与MB分别由A与B面单粒小麦籽的光谱构成,模型MC由A与B面的平均光谱构成,模型MD由A与B面的光谱混合而成。利用偏最小二乘回归法对这4种模型进行比较,结果表明:模型MD具有最佳预测效果,对于模型MD,经过1阶微分和标准正态变换预处理后,其预测集的均方根误差为1.36%,相关系数为0.90。同时利用高光谱成像技术还可以获得整盘单粒小麦籽的水分分布图,分布图能定位水分不正常的粒籽,为研究自动剔除水分异常的单粒小麦籽提供参考。     

山楂微波干燥特性及含水率预测

为探索山楂微波干燥特性，并实现干燥过程中含水率预测，研究了干燥温度（50、60、70 ℃）、相对湿度（5%、15%、30%、50%、70%）对山楂干燥特性及品质影响；建立了极限学习机（ELM）神经网络，实现对山楂含水率预测。结果表明:干燥温度60 ℃、相对湿度为30%为最佳干燥条件，山楂色泽变化最小，V_C含量最高，总黄酮含量较高。建立了结构为“3-8-1”的ELM含水率模型，其预测值与实验值之间的决定系数R2为0.996，均方根误差RMSE为0.00952，能够有效地预测山楂微波干燥过程中的含水率。研究结果为山楂微波干燥应用及含水率在线预测提供理论依据。     

Mapping of Fat and Moisture Distribution in Atlantic Salmon Using Near-Infrared Hyperspectral Imaging

A nondestructive and rapid method using near-infrared (NIR) hyperspectral imaging was investigated to determine the spatial distribution of fat and moisture in Atlantic salmon fillets. Altogether, 100 samples were studied, cutting out from different parts of five whole fillets. For each sample, the hyperspectral image was collected with a pushbroom NIR (899–1,694 nm) hyperspectral imaging system followed by chemical analysis to measure its reference fat and moisture contents. Mean spectrum were extracted from the region of interest inside each hyperspectral image. The quantitative relationships between spectral data and the reference chemical values were successfully developed based on partial least squares (PLS) regression with correlation coefficient of prediction of 0.93 and 0.94, and root mean square error of prediction of 1.24 and 1.06 for fat and moisture, respectively. Then the PLS models were applied pixel-wise to the hyperspectral images of the prediction samples to produce chemical images for visualizing fat and moisture distribution. The results were promising and demonstrated the potential of this technique to predict constituent distribution in salmon fillets.     

Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration

This study was carried out for rapid and noninvasive determination of water distribution within beef during dehydration using time series hyperspectral imaging (TS-HSI). Hyperspectral images (380–1,700 nm) of beef slices were acquired at different periods of dehydration process. The spectra of beef were extracted from the TS-HSI images using image segmentation process. Principal component analysis was conducted to obtain an overview of the systematic spectral variations during dehydration. Instead of the traditional data mining strategies to cope with the large multivariate data structures in the TS-HSI images, the selection of effective wavelengths was conducted for the first time to reduce the computational burden of the TS-HSI data and predigest calibration modeling. On the basis of the effective wavelengths identified by using successive projections algorithm (SPA), three spectral calibration algorithms of partial least squares regression, least squares support vector machines, and multiple linear regression (MLR) were compared. The SPA-MLR model with Spectral Set I was considered to be the best for determining water content of beef slice. The model led to a coefficient of determination ( $ r_V^2 $ ) of 0.953 and root mean square error estimated by cross-validation of 1.280 %. The visualization of water distribution within beef slice during dehydration was finally generated by transferring the quantitative model to each pixel in the image to determine water content in all spots of the beef sample. Kinetic analysis of the TS-HSI images was also conducted for the first time to analyze spectral changes of beef during dehydration. The results demonstrate that TS-HSI has the potential of quantitatively visualizing water content of beef rapidly and noninvasively during dehydration in a reasonable accuracy.     

高压处理对牛肉肌红蛋白及颜色变化的影响

室温下不同压力(200、400、600和800MPa)，20min处理对绞碎牛肉颜色及肌红蛋白的影响进行了研究。结果显示：随压力的上升，L^*值增加、a^*值下降，肌肉逐渐失去红色变为灰棕色。同时，肌红蛋白总量显著降低，高铁肌红蛋白的比例在压力400MPa以上时显著增加。真空包装不能减轻压力导致的变色，而添加亚硝酸钠能对色泽起到稳定作用。压力处理导致色泽变化的原因可能是由于二价铁的肌红蛋白氧化成三价铁的高铁肌红蛋白、球蛋白的变性及结构的变化所致，可通过处理前腌制形成亚硝基肌红蛋白来稳定色泽。