黑宝石李品质参数的近红外漫反射无损检测模型的建立

以黑宝石李为试材,研究其品质参数的近红外漫反射无损检测模型的建立方法,并从数学建模算法、导数处理及测试部位等方面优化模型。结果表明,改进偏最小二乘法结合一阶导数处理建立分析模型的定标效果相对较好,其中可溶性固形物(SSC)和干物质/水分模型的定标系数达0.9;四个测试部位光谱混合所建模型的预测精度显著高于单独测试部位光谱模型;SSC、干物质/水分的定标与预测的决定系数、均方根误差和相对分析误差分别在0.9、0.45和3.0左右,预测效果很好;而可滴定酸的预测误差偏大。      

枇杷内部品质近红外光谱无损检测

利用近红外光谱技术(near infrared spectroscopy,NIRS)对101个枇杷样品进行无损检测,测得样品的可溶性固形物(total soluble sdid,TSS)、可滴定酸和Vc含量,结合偏最小二乘法(partial least squares,PLS)分别建立TSS、可滴定酸和Vc含量的定标模型。采用定标模型分别对TSS、可滴定酸和Vc的验证集样品进行预测,预测决定系数Rp2分别为0.906,0.745,0.554,预测均方根误差(root-meansquare error of prediction,RMSEP)分别为0.628,0.048,2.230,且TSS的相对分析误差(relative prediction deviation,RPD)为3.31,可滴定酸和Vc的RPD分别为2.00,1.52。表明建立的枇杷TSS的定标模型可用于实际检测,枇杷的可滴定酸和Vc含量可采用近红外光谱进行检测,但检测精度有待于进一步的提高。     

樱桃多品质数据分析与无损快速检测模型的建立

目的 研究樱桃多品质数据分布情况, 建立樱桃多品质无损快速检测方法。方法 对樱桃样品分别测试可溶性固形物含量、可滴定酸含量、果实硬度。采用统计分析方法对数据进行统计学描述, 分别绘制含量分布直方图并计算直方图分布频次百分比。以樱桃样品近红外光谱数据为自变量、品质数据参考值为因变量建立樱桃品质无损快速定量检测模型。结果 统计分析结果表明, 可溶性固形物含量11~17 Brix区间范围内的样品数占样品总数的约86.0%, 可滴定酸含量0.1%~0.8%区间范围内的样品数占样品总数的约90.4%, 果实硬度1.60~3.00 kg/cm2区间范围内的样品数占样品总数的约86.0%。多元回归建模结果表明, 剔除异常值有助于提高模型预测性能, 剔除异常值后可溶性固形物含量、可滴定酸含量、果实硬度模型的相对预测性能值分别提高了15.3%、32.9%、12.3%。结论 采用统计分析结合直方图分析可较直观地描述樱桃品质分布情况; 剔除异常值对提高樱桃可滴定酸含量近红外无损检测模型预测能力的作用最大。     

冷藏樱桃内部品质的近红外漫反射光谱检测

采用沙蜜豆樱桃为实验材料,对冷藏条件下的樱桃进行定量光谱分析,在全光谱范围(408.8~2 492.8nm)下,选取TSS、TA与TSS/TA作为评价指标,分别进行校正模型的预处理讨论。结果显示:TSS、TSS/TA的最优预处理均是全波长范围下的改进偏最小二乘(MPLS)算法,在一阶微分下的去离散处理(SNV and D)。TA的最优预处理是全波长范围下偏最小二乘(PLS)算法,在一阶微分下的标准多元散射校正(SMSC)处理。TSS、TA、TSS/TA的校正误差SEC分别是,0.432 9,0.037 5,0.576 1。校正相关系数Rcv2分别是0.941 5、0.861 7,0.928 7。预测相对分析误差RPD分别是3.9,3.7,2.7。说明建立的樱桃中TSS、TA、TSS/TA 3个模型稳定性好,能够达到实际应用标准。     

近红外漫反射无损检测梨果可溶性固形物的研究

应用近红外漫反射无损检测梨果可溶性固形物。通过自行设计的NIR光谱系统测定了240个梨果样品的SSC。180个梨果样品用来建模,其余60个用来验证模型的性能。采集完整梨果的近红外漫反射光谱（350～1 800 nm）,光谱经移动窗口平滑处理、一阶微分和二阶微分预处理后,再分别采用多元线性回归、主成分回归和偏最小二乘法,建立梨果可溶性固形物的定量预测数学模型。结果表明：采用一阶微分结合偏最小二乘法所建模型的预测效果较好,可溶性固形物定量预测数学模型的相关系数为0.928 5,均方根误差为0.436 4。近红外漫反射光谱作为一种无损的检测方法,可用于评价梨果的可溶性固形物。     

蓝莓可溶性固形物、总酸近红外无损检测模型的建立

建立了近红外漫反射光谱技术检测蓝莓可溶性固形物、总酸的数学模型,并对其进行评价。实验比较了在近红外全波长范围400~2 500 nm内,不同的光谱预处理方法对模型的影响。结果表明利用偏最小二乘法(PLS)、一阶导数(D1Log(1/R))和加权多元离散校正处理(WMSC)建立的可溶性固形物含量(SSC)定标模型预测结果相对较好。其预测相关系数Rp2为0.8518,预测标准误差(SEP)为0.351,相对分析误差(RPD)为2.05。总酸的最佳模型处理条件为改进偏最小二乘法(MPLS)、二阶导数(D2Log(1/R))和WMSC,其Rp2为0.8776,SEP为0.042,RPD为2.10。由此确定近红外漫反射技术可用于蓝莓可溶性固形物、总酸含量的快速无损检测。     

贮藏期内富士和粉红女士苹果品质的FT-NIR无损检测

为探索傅里叶近红外光谱快速无损检测贮藏期苹果品质的方法,在苹果贮藏过程中,每隔30d采集富士和粉红女士(各40个)2个苹果品种共计400个样本的近红外图谱(12000～4000cm-1),用OPUS-QUANT软件预处理光谱,用偏最小二乘法建立通用于2个品种的可滴定酸(TA)、pH值和可溶性固形物(SSC)的数学模型。结果表明：富士和粉红女士的光谱经矢量归一化预处理后,在波段7502～4247cm-1内所建立的可滴定酸模型稳定性较好,该模型校正时的相关系数(R2)和评估均方误分别为0.9231和0.0263%,预测时的相关系数R2和内部交叉验证均方根差分别为0.9071和0.0266%;在波段11995～4247cm-1内,光谱经一阶导数预处理后所建立的pH值预测模型稳定性较好,该模型校正时的R2和评估均方误分别为0.9263和0.0700,预测时的R2和内部交叉验证均方根差分别为0.9113和0.0772;近红外光谱经最大-最小归一化预处理后,在波段6102～5446cm-1所建立的SSC模型效果较好,该模型校正时的R2和评估均方误分别为0.9212和0.3570%,预测时的R2和内部交叉验证均方根差分别为0.9130和0.370%。在富士和粉红女士贮藏期品质检测过程中,建立的通用于这2个品种的TA、pH值和SSC检测的数学模型,稳定性较好,能满足品质快速无损检测的要求。     

近红外漫反射无损检测赣南脐橙中可溶性固形物和总酸

目的:利用近红外漫反射无损检测技术对赣南脐橙可溶性固形物和总酸含量进行相关研究。方法:通过自行设计的NIR光谱系统测定150个赣南脐橙可溶性固形物和总酸。120个赣南脐橙样品用来建模,其余30个用来验证模型的性能。采集完整赣南脐橙的近红外漫反射光谱(350～1800nm),光谱经移动窗口平滑处理、一阶微分和二阶微分预处理后,再分别采用主成分回归(PCR)和偏最小二乘法(PLS),建立赣南脐橙可溶性固形物和总酸含量的定量预测数学模型。结果:采用一阶微分结合偏最小二乘法所建模型的预测效果较好,可溶性固形物和总酸含量定量预测数学模型的相关系数分别为0.9263和0.9562,均方根误差分别为0.4102°Brix和0.018%。结论:近红外漫反射光谱作为一种无损的检测方法,可用于评价赣南脐橙的可溶性固形物和总酸含量。     

近红外漫反射光谱检测损伤猕猴桃内部品质

研究贮藏期间损伤猕猴桃内部品质与其近红外漫反射光谱之间的关系。利用近红外光谱(12000～4000cm-1)技术和多元线性回归(multiple linear regression,MLR)、主成分回归(principal component regression,PCR)和偏最小二乘法(partial least squares,PLS)3种校正方法分别对损伤华优猕猴桃在2℃条件下贮藏4周期间的可溶性固形物含量、pH值和硬度进行定量分析;并对比吸光度原始光谱、一阶微分和二阶微分3种不同预处理方法的PLS模型校正结果。结果表明：一阶微分预处理方法时,应用PLS建立的可溶性固形物含量、pH值和硬度校正模型的效果最佳;预测集样品预测值与测量值之间的相关系数分别为0.812、0.703、0.919,预测均方根误差分别为0.749、0.153、1.700。说明应用近红外漫反射技术检测贮藏期间损伤猕猴桃的内部品质是可行的。     

生鲜紫薯花青素等多品质参数的可见-近红外快速无损检测

基于实验室自行搭建的可见-近红外光谱系统,以市售生鲜紫薯为研究对象,探讨其花青素、可溶性固形物（soluble solid contents,SSC）以及总糖（total sugars,TS）的同时快速无损检测方法。对紫薯原始光谱进行SG（Savitzky-Golay）平滑、标准正态变量变换以及一阶求导预处理,然后用偏最小二乘回归法进行建模分析。对于花青素和TS,经SG平滑结合一阶求导预处理的模型预测效果最佳;对于SSC,经SNV预处理的模型预测效果最好。针对紫薯各参数最佳预处理光谱采用竞争性自适应加权算法进行波长筛选,再次建立模型。花青素模型预测集的相关系数为0.942 1,预测均方根误差（root mean square error of prediction,RMSEP）为0.225?9?mg/g;SSC模型预测集相关系数为0.943?1,RMSEP为0.878?7?°Brix;TS模型预测集的相关系数为0.925?3,RMSEP为0.244?3%。结果显示,利用可见-近红外光谱可以实现对生鲜紫薯的花青素、SSC以及TS的同时快速无损检测,对生鲜紫薯品质的快速无损检测分选有着重要的实用意义。     

Phenological sensitivity of berry growth and composition of Tempranillo grapevines (Vitis vinifera L.) to water stress

Background and Aims:  The effect of water stress on berry quality is not fully understood. This study was designed to analyse the differential phenological sensitivity of Tempranillo berry quality to water stress during three phenological stages.
Methods and Results:  Two-year-old potted Tempranillo vines were exposed to four levels of irrigation (100, 50, 25, and 0% of evapotranspiration) during three phenological stages (Stage I, from anthesis to fruitset; Stage II, pre-veraison; Stage III, post-veraison). Vine water status was monitored by means of leaf water potential measurements. Berry quality was measured at harvest and defined by the following parameters: berry dry weight, soluble solids content, titratable acidity, polyphenol and anthocyanin concentrations in the must. Berry dry-matter accumulation was more sensitive to water stress applied during Stage I and Stage II than in Stage III. Berry quality tended to decrease linearly with increasing water stress during Stage II. During Stage III, berry quality increased linearly for light-to-mild levels of water stress, whereas quality decreased above a certain water-stress threshold (Ψ_leaf = −1.12 MPa).
Conclusions:  Tempranillo berry quality demonstrated great phenological sensitivity to water stress. Pre-veraison water stress negatively affected berry quality in Tempranillo vines, whereas post-veraison water stress increased quality up to a certain threshold of Ψ_leaf.
Significance of the Study:  For the first time, this research reports a plant-based water status threshold in Tempranillo vines above which post-veraison water stress can negatively affect berry quality.     

Evaluation of near-infrared reflectance spectroscopy for predicting stover quality trait in semi-exotic populations of maize

Near-infrared reflectance spectroscopy (NIRS) analysis was investigated as a means of predicting quality parameters in semi-exotic maize stover. These parameters included crude protein (CP), neutral detergent fibre, acid detergent fibre and in vitro dry matter digestibility (IVDMD). Samples of semi-exotic maize stover were collected during three growing seasons (1989, 1990, 1991) from three locations in Catalonia, Spain. Calibration equations were obtained by multiple linear regression of conventional laboratory values on NIRS data from 84 samples and verified with 20 additional samples. Separate NIRS calibration were developed also within year (1989 and 1990, respectively). A Bran + Luebbe InfraAnalyzer model 450 was used for the study. In the multi-year calibration the coefficients of squared multiple correlation (R²) ranged from 0–81 for IVDMD to 0–92 for CP and the standard errors of calibration (SEC) ranged from 0–35 for CP to 1–46 for IVDMD. The study showed that NIRS analysis can be used to evaluate the quality of semi-exotic maize in breeding programmes.     

Rapid measurement and control of the moisture content of compost using near-infrared spectroscopy

In a compost fermentation of soybean-curd (tofu) refuse, the effects of the moisture content of the compost on the compost reaction were studied. The moisture content of the compost was a very important factor for good fermentation. Near-infrared spectroscopy (NIRS) was applied to the determination of the moisture content of the compost. The reflected rays in the wavelength range between 400 and 2500 nm were measured at 2 nm intervals. The absorption of water was observed at three wavelengths, 960, 1406 and 1888 nm. To formulate a calibration equation, a multiple linear regression analysis was carried out between the near-infrared spectral data at 960 nm (sample number, n = 50) and on the moisture content obtained using a drying method. The values of the simple correlation coefficient and the standard error of calibration were 0.987 and 1.33%, respectively. To validate the calibration equation obtained, the moisture content in the prediction sample set (n = 35) not used for formulating the calibration equation was calculated using the calibration equation, and compared with the values obtained using the drying method. Good agreement was observed between the results of the drying method and those of the NIRS method. The simple correlation coefficient and standard error of prediction were 0.979 and 1.85%, respectively. Then, the NIRS method was applied to a practical situation in which the moisture content was measured and controlled during the compost fermentation, and good results were obtained. The study indicates that NIRS is a useful method for measurement and control of the moisture content in the compost of soybean-curd refuse.     

Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry

The relationships between the nondestructive visible and near-infrared (Vis-NIR) measurements and the internal quality indices of pear fruit were established, and the potential of Vis-NIR spectrometry technique was investigated for its ability to nondestructively measure soluble solids content (SSC) and firmness of intact pear fruit. Intact pear fruit were measured by diffuse reflectance Vis-NIR in 350–1800 nm range. In this study, calibration models relating Vis-NIR spectra to SSC and firmness were developed based on multi-linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) with respect to the logarithms of the reflectance reciprocal log(1/R), its first derivative D₁ log(1/R) and second derivative D₂ log(1/R). The best combination, based on the robust models and the prediction results, was PLSR method with respect to log(1/R) at equatorial position of pear fruit. The PLSR models for prediction samples resulted correlation coefficient (r_p) of 0.912 and 0.854, and root mean standard error of prediction (RMSEP) of 0.662°Brix and 1.232 N for SSC and firmness, respectively. The results indicate that Vis-NIR spectrometry technique could provide an accurate, reliable and nondestructive method for assessing the internal quality indices of intact pear fruit.     

近红外光谱对甜椒果实质地的无损检测

以黄色甜椒为研究对象,建立其近红外漫反射光谱检测果实质地的数学模型。在400~2 500、400~1100、400~1 450 nm 3个波段内分别建立了甜椒的果肉弹性、回复性和凝聚性定标MPLS模型,并用各波段下最优模型进行预测。结果表明:这3个波段下的定标模型相关系数都很高,但在全光谱下建立的定标模型稳定性最好,所以选取该光谱下的定标模型作为最终的测定模型,果肉弹性、回复性和凝聚性定标集交互验证相关系数(RCV)分别为0.937、0.933、0.932,交互验证标准误差(SECV)分别为0.029、0.013、0.016,预测集的相关系数RP分别为0.924、0.899、0.922,预测标准误差(SEP)分别为0.026、0.018、0.015,相对残差分别为-0.200、0.068、-0.033。结果说明,甜椒果实质地的近红外无损检测是可行的,果实质地与近红外漫反射光谱具有显著相关性。     

Methods for quality evaluation of sweet cherry

Sweet cherry (Prunus avium L.) is a highly valued fruit, whose quality can be evaluated using several objective methodologies, such as calibre, colour, texture, soluble solids content (SSC), titratable acidity (TA), as well as maturity indexes. Functional and nutritional compounds are also frequently determined, in response to consumer demand. The aim of the present review is to clarify and establish quality evaluation parameters and methodologies for the whole cherry supply chain, in order to promote easy and faithful communication among all stakeholders. The use of near-infrared spectroscopy (NIRS) as a non-destructive and expeditious method for assessing some quality parameters is discussed. In this review, the results of a wide survey to assess the most common methodologies for cherry quality evaluation, carried out among cherry researchers and producers within the framework of the COST Action FA1104 ‘Sustainable production of high-quality cherries for the European market’, are also reported. The standardisation of quality evaluation parameters is expected to contribute to the preservation and shelf-life extension of sweet cherries, and the valorisation of the whole supply chain. For future studies on sweet cherry, we put forward a proposal regarding both sample size and the tests chosen to evaluate each parameter. © 2022 Society of Chemical Industry.     

Moisture content detection of maize seed based on visible/near-infrared and near-infrared hyperspectral imaging technology

To realise accurate and nondestructive detection on moisture content of maize seed based on visible/near-infrared (Vis/NIR) and near-infrared (NIR) hyperspectral imaging technology, the hyperspectral images on two sides (embryo and endosperm sides) of each maize seed of four varieties were collected. The effects of average spectra extraction regions, that is centroid region and whole seed region, and different spectral preprocessing methods, were investigated. Uninformative variable elimination (UVE) was used to extract the feature wavelengths, and the partial least squares regression (PLSR) prediction models were established. The results showed that extracting the average spectra from the centroid region did better than from the whole seed region, and S-G smoothing was prior to other preprocessing methods. The PLSR models established with NIR spectra had better performance than that with Vis/NIR spectra. The model developed for a single variety was superior to that for all varieties together.