近红外光谱技术在农产品加工中的应用

简述了现代近红外分析技术,包括光谱特征,定量和定性分析方法,分析了影响近红外光谱仪精度的主要因素。着重介绍了这门新技术在农产品检测领域中的应用。最后介绍了近红外技术对苹果进行在线检测分级系统。     

近红外光谱技术在农产品加工中的应用

简述了现代近红外分析技术 ,包括光谱特征 ,定量和定性分析方法 ,分析了影响近红外光谱仪精度的主要因素。着重介绍了这门新技术在农产品检测领域中的应用。最后介绍了近红外技术对苹果进行在线检测分级系统     

近红外光谱技术在小麦品质检测中的应用进展

小麦作为中国三大粮食作物之一,也是制作各种面类食品的初级原料。小麦品质检测对农业生产和食品加工有重要影响,是小麦生产和小麦籽粒加工的重要环节。近年来,近红外光谱技术已广泛应用于小麦品质检测,相比其他检测方式,近红外光谱检测具有快速、无损、精确的特点,符合现代加工产业和科研单位对小麦籽粒的储存,品质测定和加工的需求。文章主要综述了近红外光谱技术应用于小麦品质检测方面的研究现状,以及近红外光谱技术应用于检测小麦品质时存在的问题,以期为今后无损化小麦籽粒品质检测和优化近红外检测粮食技术提供理论依据。     

近红外光谱技术在油脂检测中的应用

近红外光谱技术作为一种快速检测技术，在食品行业已应用于粮食检测、果蔬加工和肉制品加工等领域，为食品质量管理作出了较大贡献，本文就近红外光谱技术在油脂油分含量及其品质检测方面的应用加以综述。     

近红外光谱分析技术在苹果品质检测中的应用进展

苹果营养丰富,是大众水果之一,苹果的品质安全问题一直是社会关注的热点。本研究综述了2012~2016年我国近红外光谱分析技术在苹果品质检测中的研究和应用进展,包括基于近红外光谱的苹果品质检测、模型传递、分类与分级及在线检测系统研制4个方面。对今后我国近红外光谱分析技术在苹果品质检测中的应用提出建议,技术研究方面应不断建立和维护模型数据库,提高模型通用性;技术应用方面应研制在线检测系统和推进软硬件+互联网相结合,使近红外光谱分析技术在苹果品质检测中得到长足的发展。     

近红外光谱技术在稻米品质快速检测中的应用

综述近红外技术在稻米品质的常规指标,如直链淀粉、蛋白质、脂肪酸、胶稠度中的研究与应用,总结不同样品形态和建模方法建立的近红外模型的检测精度,指出近红外技术在快速检测稻米品质中存在的问题,以及今后的研究方向。     

近红外光谱技术在食品微生物检测中的应用

食品微生物受环境和时间的影响等因素产生变化,幵且检测周期长,导致关系食品质量安全的微生物检测数据不能相对真实地反映食品微生物情况。近些年,近红外检测技术因其高效、无损、快捷特点,在食品微生物检测领域越来越受到重视。文章综述了近红外检测技术的原理、特点和检测过程,以及食品中产毒真菌、食源性致病菌和食品腐败菌等检测斱面的应用,以期为近红外检测技术在食品微生物检测中的应用研究提供参考,促迚食品微生物检测采用近红外迚行在线快速检测。     

近红外光谱技术在粮油检测中的应用

分析了近红外光谱技术的工作原理和技术特点，着重介绍近年来近红外光谱技术在粮油品质分析中的应用，并分析了该技术的发展趋势及应用前景。     

近红外光谱技术在油脂检测中的应用

近红外光谱技术作为一种快速检测技术 ,在食品行业已应用于粮食检测、果蔬加工和肉制品加工等领域 ,为食品质量管理作出了较大贡献 ,本文就近红外光谱技术在油脂油分含量及其品质检测方面的应用加以综述。     

近红外光谱技术在酿酒葡萄品质检测中的应用现状及展望

文章介绍了近红外光谱技术的基本原理、测定程序以及该分析方法中的定量分析法和定性判别法,综述了国内外采用该技术评价酿酒葡萄品质指标,鉴定酿酒葡萄品种以及检测酿酒葡萄成熟度等方面的应用进展情况,分析了该技术在检测酿酒葡萄品质时存在的问题以及应用前景。     

Application of reflectance near infrared spectroscopy for bread analyses

The feasibility of using near infrared spectroscopy (NIRS) for prediction of nutrients in a wide range of bread varieties mainly produced from wheat and rye was investigated. Calibration was performed on samples collected over a 3-year-period and the calibration equations were tested on samples collected the subsequent year. Bread samples were dried, crushed, ground and measured in a rotating sample cup in the wavelength range 1100–2500 nm. Full range or segmented reflectance data was used for calibration based on partial least square (PLS) regression. Protein, fat, dietary fibre, sugar, ash, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA) and Na could be determined directly with r² values of 0.99, 0.99, 0.89, 0.96, 0.91, 0.90, 0.91, 0.92 and 0.76, respectively. The total contents of carbohydrates and energy was calculated from NIR data with r² values of 0.98 and 0.99. The ratios between analyte variation range standard deviation (SD) and the root mean square error of cross validation (RMSECV) were 8.3 (protein), 9.1 (fat), 3.0 (dietary fibre), 4.7 (sugar), 3.0 (ash), 3.1 (SFA), 3.3 (MUFA), 3.5 (PUFA), 1.9 (Na), 7.2 (carbohydrates) and 8.4 (energy). Equivalent ratios were obtained on an independent test set. It is concluded that the applied NIRS methodology is suitable for routine analysis of wheat and rye based bread for the investigated organic properties and ash. The technique may also give a rough estimate of the Na content.     

Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review

Over the past three decades, near infrared reflectance (NIR) spectroscopy has been proved to be one of the most efficient and advanced tools for the estimation of quality attributes in meat and meat products. This review focuses on the use of NIR spectroscopy to predict different meat properties, considering the literature published mainly in the last decade. Firstly, the potential of NIR to predict chemical composition (crude protein, intramuscular fat, moisture/dry matter, ash, gross energy, myoglobin and collagen), technological parameters (pH value; L*, a*, b* colour values; water holding capacity; Warner–Bratzler and slice shear force) and sensory attributes (colour, shape, marbling, odour, flavour, juiciness, tenderness or firmness) are reviewed. Secondly, the usefulness of NIR for classification into meat quality grades is presented and thirdly its potential application in the industry is shown. The review indicates that NIR showed high potential to predict chemical meat properties and to categorize meat into quality classes. In contrast, NIR showed limited ability for estimating technological and sensory attributes, which may be mainly due to the heterogeneity of the meat samples and their preparation, the low precision of the reference methods and the subjectivity of assessors in taste panels. Hence, future work to standardize sample preparation and increase the accuracy of reference methods is recommended to improve NIR ability to predict those technological and sensory characteristics. In conclusion, the review shows that NIR has a considerable potential to predict simultaneously numerous meat quality criteria.     

一种国产近红外仪分析油菜籽三种品质参数

利用中国农业科学院油料作物研究所研制的NYDL-3000智能型多参数粮油品质速测仪分析了油菜籽含油量、硫甙和芥酸含量,测定结果与传统化学方法进行了比较,其中含油量结果与PC-120核磁共振仪和MA-TRIX-I傅立叶变换近红外光谱仪做验证。内部交叉验证结果显示：含油量、硫甙和芥酸含量的决定系数R2分别为0.954 3、0.985 3和0.982 3,均方差分别为0.046 9、0.419 8和0.160 4,T检验显示三种品质参数交叉验证集的近红外光谱法（NYDL-3000）预测值和化学值差异均不显著。外部检验结果表明：硫甙含量、含油量两种方法测定值的相关系数分别为0.837 6和0.951 7,平均绝对误差分别为7.54%和0.02%,均在平均绝对误差允许的范围之内;成对数据双尾t检验表明,近红外光谱法和化学法两者结果差异不显著。芥酸分析中,NYDL-3000将342份样品中3份低芥酸材料判定为高芥酸,近红外光谱法和化学法两者结果符合率达99.12%。含油量的测定结果显示：NYDL-3000和PC-120、MATRIX-I结果的相关系数分别为0.956 6和0.901 5,平均绝对误差分别为0.78%和3.06%。研究结果表明,利用NYDL-3000智能型多参数粮油品质速测仪所建立的品质模型可以满足油菜育种对早代材料芥酸、硫甙含量和含油量的快速测定要求。     

On-line prediction of beef quality traits using near infrared spectroscopy

The aim of this study was to test the ability of visible-near infrared (Vis-NIR) reflectance spectroscopy to predict beef quality traits in the slaughterhouse by directly applying a fiber-optic probe on the carcass surface. Carcasses from 230 young bulls and heifers slaughtered in two commercial abattoirs were included in the experiment. Vis-NIR spectra were recorded on an exposed surface of M. gracilis in the abattoirs 4 to 6 and 14 to 16 h post mortem. Traits evaluated were pH, color indexes (L*, a*, b*, H, and SI), cooking loss, and Warner–Bratzler shear force. Prediction models were satisfactory for pH and color indexes, and promising for cooking loss but not for Warner–Bratzler shear force. Results of this work show that Vis-NIR spectroscopy may be a useful tool for on-line prediction of some physical beef quality traits when applied directly on the carcass surface.     

基于近红外光谱技术的主产区玉米品质分析

为全面了解我国玉米的品质情况,从全国13个玉米主产省（市）收集了2022年收获的532份玉米样品,利用近红外光谱技术快速检测玉米的容重、粗蛋白、赖氨酸、脂肪酸值、表观代谢能等11项品质指标,采用相关性分析和主成分分析等方法对所测得的数据进行分析与评价。结果显示,按照GB 1353-2018 《玉米》判定,一等玉米占比38.53%,二等玉米占比37.21%,基于国标GB/T 20570-2015《玉米储存品质判定规则》判定,样品均宜存;部分指标间呈明显的相关性,如容重与粗蛋白呈显著正相关,粗蛋白与淀粉呈显著负相关;不同主产区间比较发现,西北地区容重明显高于其他地区,北方地区玉米的淀粉含量较高,黄淮海地区粗蛋白含量仅次于西北地区;基于主成分分析方法,北方地区与黄淮海地区两大主产区的玉米品质上有明显区别。本文基于近红外光谱技术快速检测玉米的11项品质指标,并对数据做分析评价,旨在为玉米的育种、仓储及加工提供参考。     

The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes

Visible and near infrared reflectance spectroscopy was used to predict pH at 24 h (pH24) post-mortem, sarcomere length (SL), cooking loss (CL), Warner–Bratzler Shear Force (WBSF) and colour parameters (L^∗, a^∗, b^∗) in beef cattle samples. Samples from M. longissimus thoracis et lumborum from 30 bulls were aged at 4 °C for 1, 3, 7 and 14 days and analysed for pH, SL, CL, WBSF and colour. NIRS calibrations for pH24, luminosity at 0 (L^∗t0) and 60 min (L^∗t60) showed good predictability (R² = 0.97, 0.85 and 0.82; SECV = 0.10, 1.16, 1.36, respectively), whereas those related to the rest of the parameters were poorer.     

近红外光谱技术在水产品检测中的应用进展

水产品营养丰富, 味道鲜美, 深受人们喜爱。由于其组织结构脆弱, 且含有丰富的内源酶和嗜冷细菌, 在储藏和运输过程中容易腐烂变质。随着水产品贸易的日益全球化, 水产品品质现已成为大家关注的核心问题。传统的水产品检测方法具有主观性、侵入性、耗时性, 已不能满足水产品市场的需求, 如何快速、客观地评价水产品品质已成为亟待解决的问题。近红外光谱技术作为一种快速、无损的检测技术已广泛应用于水产品检测, 本文综述了国内外学者采用近红外光谱技术在水产品新鲜度评价、质量评估、重金属含量检测、品种鉴别等方面的研究进展, 并分析了近红外光谱技术在水产品检测中的不足之处及发展前景, 以期为水产品快速检测技术的进一步发展和研究提供参考依据。     

基于近红外光谱建立纸张表面施胶量测定模型

采集不同施胶量纸张的近红外光谱,利用偏最小二乘法建立测定纸张表面施胶量基于近红外光谱的校正模型。得到校正模型的交叉验证均方差（RMSECV）和外部验证均方差（RMSEP）分别为0.0928和0.1460,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别为0.9609和0.9294,表明所建立的校正模型具有较高的预测精度和较好的推广性,为纸张无损伤、无预处理的快速、简便、准确的检测提供了新的途径,并且可望实现纸机上的在线检测。     

Application of near infrared spectroscopy to peanut grading and quality analysis: overview

Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.