Journal of Near Infrared Spectroscopy最新文献_第7页

Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir 用近红外反射光谱法评价大尾草、雀稗、茯苓等牧草品质

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-29 DOI: 10.1177/09670335221083070

A. Oluk, Hatice Yucel, Feyza D Bilgin, U. Serbester

{"title":"Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir","authors":"A. Oluk, Hatice Yucel, Feyza D Bilgin, U. Serbester","doi":"10.1177/09670335221083070","DOIUrl":"https://doi.org/10.1177/09670335221083070","url":null,"abstract":"Dallisgrass (Paspalum dilatatum Poir.) is an economically important and widely cultivated forage crop for livestock feeding in the tropical, subtropical, and warm temperate regions because of good adaptation to unsuitable pasture conditions. In this study, 216 dallisgrass samples were used to develop near infrared reflectance calibrations to estimate five forage quality parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and ash. Second derivative pretreatment was applied for calibration of DM, CP and NDF while a first derivative pretreatment was used for ADF and ash. The coefficients of determination in the internal validation set (r 2 ) were 0.78 for DM, 0.80 for CP, 0.95 for NDF 0.75 for ADF, and 0.71 for ash. The relative predictive determinant ratios for calibration indicate that the NDF equations were acceptable for quantitative prediction of dallisgrass quality, whereas the DM, CP, ADF, and ash equations were useful for screening purposes. The near infrared prediction models developed in this study can be used for screening in the forage breeding researches to be carried out for five quality parameters in the future.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44745090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy 霍山石斛（冯斗）的快速无损手持近红外光谱定性分类

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-25 DOI: 10.1177/09670335221078354

Fang Wang, Bin Jia, Jun Dai, Xiang-wen Song, Xiaoli Li, Haidi Gao, Hui Yan, Bangxing Han

{"title":"Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy","authors":"Fang Wang, Bin Jia, Jun Dai, Xiang-wen Song, Xiaoli Li, Haidi Gao, Hui Yan, Bangxing Han","doi":"10.1177/09670335221078354","DOIUrl":"https://doi.org/10.1177/09670335221078354","url":null,"abstract":"Because of the similar appearance and properties of different quality grades of the product, super Dendrobium huoshanense could be easily adulterated with first-grade D. huoshanense and second-grade D. huoshanense products, thereby affecting its clinical application and causing market distortion. In this study, a combination of hand-held near infrared spectroscopy and chemometrics was used to classify different grades of D. huoshanense. The standard normal variate was employed to preprocess the original near infrared spectra, following which linear analysis models (principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and a non-linear support vector machine (SVM) model, were utilized to establish the identification models. The results showed that PCA analysis could not identify the three grades of D. huoshanense, and the LDA analysis could distinguish the second-grade from the other two grades. The PLSDA model resulted in prediction accuracies for the calibration cross-validation, and test sets of 91.83%, 83.58%, and 84.29%, respectively. Unfortunately, the super and first-grade D. huoshanense were not identified by the linear analysis model. Further analysis was performed with a non-linear model, where SVM was used to analyze all grades of D. huoshanense. The recognition rate of thel training set and validation set were 88% and 84%, respectively. All in all, the use of a hand-held near infrared spectrometer combined with chemometrics could identify the quality grade of D. huoshanense samples on-site in real-time, and provide a simple, fast, and reliable method for the quality control of the traditional Chinese medicine herb of D. huoshanense.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49660004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Discrimination of centre composition in panned chocolate goods using near infrared spectroscopy 近红外光谱法判别焙烤巧克力制品中的中心成分

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-25 DOI: 10.1177/09670335221085616

Joel B. Johnson

{"title":"Discrimination of centre composition in panned chocolate goods using near infrared spectroscopy","authors":"Joel B. Johnson","doi":"10.1177/09670335221085616","DOIUrl":"https://doi.org/10.1177/09670335221085616","url":null,"abstract":"Non-destructively identifying the centre composition of panned chocolate goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near infrared spectra (1000–2500 nm) were collected from chocolate-coated peanuts and chocolate-coated sultanas (n = 170 of each) in order to investigate the prospect of non-invasively detecting the composition of the centre. Principal component analysis confirmed that the spectra of these samples were distinct from one another. The partial least squares discriminant analysis (PLS-DA) model showed a high level of separation between chocolate-coated peanuts and sultanas in the training set (R2 = 0.95; RPD = 4.4). Discrimination between peanut and sultana samples from an independent test set was also possible, although with slightly less distinct separation between the sample types. A soft independent modelling by class analogy model was also able to differentiate between the two sample types, albeit with higher levels of misclassification compared to PLS-DA. Incorporating samples from different manufacturers may be useful for improving the broader applicability of the model.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47382783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Investigating the water structures in reverse micelles by temperature-dependent near infrared spectroscopy combined with independent component analysis 利用温度相关近红外光谱结合独立组分分析研究了反胶束中的水结构

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-22 DOI: 10.1177/09670335221082220

Mian Wang, Yan Sun, Chaoshu Duan, W. Cai, Xueguang Shao

引用次数: 3

Assessing the potential of a handheld visible-near infrared microspectrometer for sugar beet phenotyping 评估手持可见近红外显微光谱仪用于甜菜表型分析的潜力

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-19 DOI: 10.1177/09670335221083448

Belal Gaci, Sílvia Mas García, F. Abdelghafour, J. Adrian, F. Maupas, J. Roger

{"title":"Assessing the potential of a handheld visible-near infrared microspectrometer for sugar beet phenotyping","authors":"Belal Gaci, Sílvia Mas García, F. Abdelghafour, J. Adrian, F. Maupas, J. Roger","doi":"10.1177/09670335221083448","DOIUrl":"https://doi.org/10.1177/09670335221083448","url":null,"abstract":"Phenotyping is essential in the process of varietal selection. In the case of sugar beets, richness (g/100g), that is, sugar content, is the key information. The need to acquire this information in a rapid, non-destructive and cheap manner leads the sugar industry to look for portable solutions that enable the suitable field measurements. In this work, a low-cost handheld and narrow visible-NIR spectral range microspectrometer is assessed for its ability to provide such information. During a two-year campaign from 2017 to 2018, a total of 649 samples of sugar beet were measured. The resulting data, along with the reference values for richness, were used to build a predictive model with partial least squares (PLS) regression. Acceptable performance in the estimation of richness from both 2017 data (SEP = 0.84 g/100 g) and 2018 data (SEP = 0.90 g/100 g) is achieved. This study also shows that updating the spectral database is possible by calibration transfer models. From the different tested transfer strategies, the combination of model update and slope-bias correction achieves the best performance, demonstrating that the use of 2017 model on different years is possible and only 75 new sugar beets are necessary to guarantee a richness error lower than 1.05 g/100 g. This work suggests that the molecular sensor could offer a useful tool for a rapid, low cost and non-destructive prediction of richness in sugar beets.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48080345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks 利用深度卷积神经网络对大块谷物样本进行高光谱成像分类

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-04-18 DOI: 10.1177/09670335221078356

E. Dreier, K. Sørensen, Toke Lund-Hansen, B. Jespersen, K. S. Pedersen

{"title":"Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks","authors":"E. Dreier, K. Sørensen, Toke Lund-Hansen, B. Jespersen, K. S. Pedersen","doi":"10.1177/09670335221078356","DOIUrl":"https://doi.org/10.1177/09670335221078356","url":null,"abstract":"Near Infrared hyperspectral imaging (HSI) offers a fast and non-destructive method for seed quality assessment through combining spectroscopy and imaging. Recently, convolutional neural networks (CNN) have shown to be promising tools for red-green-blue (RGB) image or spectral cereal classification. This paper describes the design and implementation of deep CNN models capable of utilizing both the spatial and spectral dimension of HSI data simultaneously for analysis of bulk grain samples with densely packed kernels. Classification of eight grain samples, including six different wheat varieties, were used as a test case. The study shows that the CNN architecture ResNet, originally designed for RGB images, can be adapted to use the full spatio-spectral dimension of the HSI data through adding a linear down sample layer prior to the conventional ResNet architecture. Using traditional spectral pre-processing methods before passing the data to the CNN does not improve the classification accuracy of the networks, while a channel-wise image standardization improves the accuracy significantly. The modified ResNet applied to the full spatio-spectral dimension has a classification accuracy of up to 99.75 ± 0.02%, outperforming both purely spectral (86.5 ± 0.1%) and purely spatial (98.70 ± 0.01%) based methods in terms of accuracy, indicating that utilizing spatio-spectral correlation can improve sample classification, but also that grain classification is primarily solved using spatial information. The findings reported in this paper demonstrate how CNN networks can be designed to leverage spatio-spectral information in hyperspectral data. The combination of HSI and spatio-spectral CNN networks shows a possible method for fast prediction of bulk grain quality parameters where both spectral and spatial properties of the grains are important.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43734480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Review of portable near infrared spectrometers: Current status and new techniques 便携式近红外光谱仪综述:现状与新技术

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-03-09 DOI: 10.1177/09670335211030617

C. Zhu, Xiaping Fu, Jianyi Zhang, Kai-Wen Qin, Chuanyu Wu

引用次数: 18

Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy 近红外光谱法预测福尔马林中甲醛和甲醇残留量

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-03-03 DOI: 10.1177/09670335221078355

R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho

{"title":"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy","authors":"R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho","doi":"10.1177/09670335221078355","DOIUrl":"https://doi.org/10.1177/09670335221078355","url":null,"abstract":"Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48244888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness 使用大量集对无损近红外光谱进行外部验证，以创建稳健的校准模型，从而预测苹果硬度

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-02-28 DOI: 10.1177/09670335211054299

Martina Marečková, Veronika Danková, L. Zelený, P. Suran

{"title":"Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness","authors":"Martina Marečková, Veronika Danková, L. Zelený, P. Suran","doi":"10.1177/09670335211054299","DOIUrl":"https://doi.org/10.1177/09670335211054299","url":null,"abstract":"Non-invasive flesh firmness prediction using near infrared spectroscopy has been perfected and validated on three apple varieties. Three novel calibration models were developed following three year's of repeated large-scale sampling of stored commercial apple varieties ‘Gala’, ‘Red Jonaprince’ and ‘Jonagored’. The spectroscopic results were compared directly with those obtained using the invasive method. Increased accuracy of calibration models was achieved with the newly established data collection model. The results exhibited coefficient of determination for calibration, R2, and ratio of prediction to deviation (RPD) in excess of 0.91 and 2.3, respectively, thus enabling excellent prediction of flesh firmness via a non-invasive and fast spectroscopic approach. The highest R2 obtained was 0.94, RPD 2.6, root mean square error of calibration 5.87 N, and root mean square error of cross-validation (internal) 6.75 N for variety ‘Red Jonaprince’. Our complex long-term study provided excellent external validated calibration models and the approach can help developing calibration models for commercial sorting lines using near infrared spectroscopy.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49006724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Development of a calibration model for near infrared spectroscopy using a convolutional neural network 使用卷积神经网络开发近红外光谱校准模型

IF 1.8 4区化学

Journal of Near Infrared Spectroscopy Pub Date : 2022-02-25 DOI: 10.1177/09670335211057234

Meng-hong Li, Tianhong Pan, Yang Bai, Qi Chen

引用次数: 6