近红外光谱法无损检测番茄可溶性固形物含量的研究

应用近红外漫反射光谱技术对番茄可溶性固形物含量进行了非破坏性检测分析,比较了10种不同的光谱预处理方法对偏最小二乘法(PLS)模型的影响.结果表明,常数偏移消除是适合建立近红外光谱法无损检测番茄可溶性固形物含量PLS模型的最优光谱预处理方法,最能反映番茄可溶性固形物含量信息的光谱波段为11998.9-5449.8cm-1和4601.3～4246.5 cm-1.用偏最小二乘法建立的定量分析模型,其预测值和实测值的相关系数为0.954,校正标准差为0.321%,预测标准差为0.475%.近红外漫反射光谱法可非破坏性分析番茄中可溶性固形物的含量.     

Rapid Determination of Carbon,Nitrogen, Silicon,Phosphorus, and Potassium in Sugar Mill By-products,Mill Mud,and Ash using Near Infrared Spectroscopy

This article describes a proof-of-concept exercise to examine the ability of near infrared spectroscopy (NIRS)–based methods to predict the major nutrient properties of sugar mill by-products, particularly mill mud, ash, and mixtures of mud and ash. Sixty mill mud, mixed mud/ash, and ash samples were subsampled three times and analyzed using traditional analytical techniques for carbon (C), nitrogen (N), silicon (Si), phosphorus (P), and potassium (K), and the NIR spectra were recorded. Two different partial least squares (PLS) regression models were constructed, one using all samples and the other without the ash samples included in the model development. Three mud, one mixed mud/ash, and two ash samples were retained for predictive purposes and were not included in the model development process. R² values in the range of 0.77 to 0.98 were obtained for all constituents across both sets of PLS models. The standard errors of prediction (SEP) were similar for both models for N (0.10 and 0.08), P (0.17 and 0.16), and K (0.05 and 0.05). However, the SEP obtained for Si (3.53 and 1.04) and C (1.92 and 1.00) varied between the two models. These preliminary results are very encouraging. Future research will extend to robust NIRS calibrations for these nutrients and develop applications for their use within laboratory or field situations to permit nutrient monitoring in various sugar mill by-products.     

Seed colour assessment in Brassica napus using a Near Infrared Reflectance spectrometer adapted for visible light measurements

Summary Improved oil, protein and fibre contents are associated with light seed colour in rapeseed but the lack of reliable and efficient methods to measure seed colour has hindered breeding efforts for this trait. The feasibility of using light reflectance to assess seed colour in Brassica napus was examined using scanning light reflectance spectrophotometry and near infrared reflectance (NIR). Light reflectance by seed samples from 30 doubled haploid (DH) lines segregating for seed colour increased as the wavelength of the illuminating light in the scanning spectrophotometer increased between 550 and 650 nm. The largest reflectance values were measured for the yellow seed samples; the brown seed samples were intermediate and the black seed samples had the lowest reflectance values. The areas under the reflectance curves were used to transform the spectra to single values. Average light reflectance area values for the seed colour classes were significantly different from each other. The DHs and their corresponding light reflectance area values were also used to calibrate a NIR analyzer modified with 670 and 710 nm filters. The best calibration curve used three wavelengths (670, 2190 and 2208 nm) and had a multiple correlation coefficient of 0.987. Light reflectance area values determined with the calibrated NIR analyzer for 30 randomly selected breeding lines could be used to categorize the colour of the seed samples with no discrepancies between the visual and instrument classifications. The results indicate that NIR can be used to assess seed colour in rapeseed.     

基于光谱技术的褐壳血斑蛋鉴别方法研究

血斑蛋是一种带有血丝的异常蛋。通过自制的鸡蛋内部品质光谱检测系统,采集40个正常蛋和60个人工注射血样的血斑蛋的可见/近红外光谱,研究比较了3种不同的血斑蛋判别方法:传统的血值判别法、偏最小二乘判别法(DPLS)以及融合光谱信息与蛋壳颜色信息的最小二乘支持向量机(LS-SVM)判别法,结果表明基于颜色信息融合的最小二乘支持向量机的判别结果明显优于传统的血值判别法,正常蛋的判别正确率为90%,血斑蛋的判别正确率为91.7%,证明了此方法可用于褐色蛋的血斑检测。     

近红外光谱分析技术在木材机械性能检测中的研究进展

近红外光谱技术具有快速、无污染、成本低廉、准确性高等优点,相比其他无损检测方法,被广泛应用于农业、医学、化工、造纸等各个领域。国内外许多科研工作者在木材材性分析和检测方面也作了大量的探索。本文主要介绍木材材性分析的重要性、近红外光谱技术的基本原理和特点,国内外林业科技工作者在辐射松、蓝桉、火炬松和粗皮桉等不同树种木材的抗弯强度、抗弯弹性模量、密度和压缩强度等物理力学性质检测方面所做出的贡献和取得的进展,通过介绍可以看出NIR技术具有很大的潜能,它可以快速、准确的获悉木材的性质,从而对木材进行科学合理的利用。     

基于光谱特征参数的温室番茄叶片叶绿素含量预测

为了快速、准确估测温室番茄叶片叶绿素含量,提升作物精细管理水平,利用光谱分析技术研究了温室番茄不同生长阶段叶绿素含量和响应光谱的相关性,在幼苗营养生长阶段叶片叶绿素含量呈增长趋势,到移植50天前后达到最大值,在此期间反射光谱的红边会向红外方向（长波）偏移,同时绿峰向蓝光（短波）方向偏移,绿峰幅值减小。从结果期开始叶绿素含量呈下降趋势,而红边、绿峰及绿峰幅值向相反方向变化。为了定量分析叶绿素含量和叶片反射光谱间的关系,从自定义的68个光谱特征参数中提取了7个能反映叶绿素含量变化的最优参量,并使用逐步回归、岭回归、主成分分量回归和偏最小二乘回归消除了最优参量的多重共线性,建立了叶绿素含量预测模型,其中岭回归模型精度最佳,均方根误差（RMSE）为0.406,决定系数(R2)为0.839。     

利用近红外无损检测技术测定水稻土全氮含量的研究

探讨了利用近红外光谱法测定水稻土全氮含量的可行性,利用Nicolet公司生产的傅里叶变换近红外透射光谱仪测定129个水稻土样品的近红外光谱值,通过几种不同的光谱前处理,采用偏最小二乘回归(PLSR)法把测得的水稻土光谱值与实验室法测得的全氮数值拟合建立定标模型,经分析得出利用标准化处理的光谱数据与全氮含量之间建立的模型稳定性最好,模型相关参数为:预测标准差SEP=0.010 9,校正相关系数RCAL=0.98,内部交叉验证相关系数RVAL=0.86,校正标准差SEE=0.031 6。结果表明:近红外法测定结果与常规方法具有很好的相关性,可以快速、无损、准确地测定水稻土全氮含量。     

Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes

Fruit fly infestation of mangos is a major concern for growers and exporters, leading to requirements for quarantine treatments such as vapor heat treatment or irradiation and subsequent reduction in quality and consumer acceptance. An on-line method for detection and removal of infested fruit would thus benefit producers and consumers. An algorithm has been developed to identify spots generated in hyperspectral images of mangoes infested with fruit fly larvae. The algorithm incorporates background removal, application of a Gaussian blur, thresholding, and particle count analysis to identify locations of infestations. Each of the four algorithm steps involves adjustable parameters which were iteratively tested to find the optimal combination for detection in terms of false positive and false negative results. For algorithm parameters selected to minimize false negative results, a false negative error rate of 1.0% was achieved with 11.1% false positive error and 6.0% overall error in heavily infested samples. For the same sample set, the lowest overall error rate achieved was 2.0%, with 1.0% false positive and 3.0% false negative. For samples with lower infestation rates, the error rates were much higher, the lowest overall error being 12.3%. This therefore demonstrates the feasibility of hyperspectral imaging for fruit fly detection while highlighting the need for technology with improved resolution and signal to noise ratio to allow detection of single larvae.     

Effects of bruise type on discrimination of bruised and non-bruised ‘Golden Delicious’ apples by VIS/NIR spectroscopy

Visual (VIS) and near-infrared (NIR) spectroscopy was applied to discriminate bruises and non-bruised healthy spots on ‘Golden Delicious’ apples. Two types of bruises were examined; those created by controlled impact and those by compression. Reflectance spectra of apples were measured in the range from 400 to 1700 nm. The data were analysed with canonical discriminant analysis (CDA). The squared canonical correlation (CR²) was 0.74 for discriminating impact bruises and non-bruised tissue, and a CR² of 0.68 was obtained for distinguishing compression bruises and sound tissue. Based on the linear discriminant functions, built with canonical components, the misclassification errors for non-bruised apples were mainly due to the presence of compression bruises. The classification accuracy was improved by taking the type of bruises into account in the model.     

纤维红外吸收特性及其在皮棉杂质检测中的应用

皮棉异性纤维杂质检测技术是近几年来国内外研究的难点。为有效检测皮棉中与棉纤维外观极其相似的异性纤维杂质，提出了一种显微近红外成像方法用于检测皮棉中异性纤维。该方法将棉纤维与异性纤维在特定红外波段的吸收特性差别，转化为近红外光谱成像系统中两者的灰度、形态图像特征差别，通过显微光路对图像特征差别放大，利用图像分割技术将异性纤维目标分割出来。试验结果表明，采用显微近红外成像方法捕获的图像中，异性纤维灰度、形态特征明显，其检测结果与实际相符，此方法可有效识别皮棉中异性纤维杂质。