Pub Date : 2023-02-20DOI: 10.1177/09670335231156470
M. Bragolusi, A. Tata, A. Massaro, Carmela Zacometti, R. Piro
Nutritional information provided on food labels can impact healthy dietary decisions of consumers. The accuracy of the information provided is of paramount importance to guide consumers’ food choices and prevent food-related chronic diseases. The present study aimed to verify the veracity of nutritional labels of 103 food products purchased online through well-known e-commerce websites (80 processed and 23 unprocessed items) using near infrared spectroscopy. Among processed food products, surprisingly, 28 food products out of 80 (35%) did not bear nutritional labels. Considering the European tolerances for nutrient values declared on a label, the comparison of experimental values with those reported on the labels showed that more than 74% of the values declared on the label were congruent with the NIR experimental data, whereas 7.5% of the label values were non-compliant with the tolerance limits, and about 11.3% were slightly outside the tolerance limits. Note that 6.6% of the values indicated in the labels did not abide the regulation at all. Finally, 35.8% of the samples showed at least one value outside the tolerance limits. The current study demonstrated the capability of NIR spectroscopy for monitoring the compliance of nutritional labels with EU tolerance limits and guiding the choice of reference methods for further confirmation purposes. Graphical Abstract
{"title":"Nutritional labelling of food products purchased from online retail outlets: screening of compliance with European Union tolerance limits by near infrared spectroscopy","authors":"M. Bragolusi, A. Tata, A. Massaro, Carmela Zacometti, R. Piro","doi":"10.1177/09670335231156470","DOIUrl":"https://doi.org/10.1177/09670335231156470","url":null,"abstract":"Nutritional information provided on food labels can impact healthy dietary decisions of consumers. The accuracy of the information provided is of paramount importance to guide consumers’ food choices and prevent food-related chronic diseases. The present study aimed to verify the veracity of nutritional labels of 103 food products purchased online through well-known e-commerce websites (80 processed and 23 unprocessed items) using near infrared spectroscopy. Among processed food products, surprisingly, 28 food products out of 80 (35%) did not bear nutritional labels. Considering the European tolerances for nutrient values declared on a label, the comparison of experimental values with those reported on the labels showed that more than 74% of the values declared on the label were congruent with the NIR experimental data, whereas 7.5% of the label values were non-compliant with the tolerance limits, and about 11.3% were slightly outside the tolerance limits. Note that 6.6% of the values indicated in the labels did not abide the regulation at all. Finally, 35.8% of the samples showed at least one value outside the tolerance limits. The current study demonstrated the capability of NIR spectroscopy for monitoring the compliance of nutritional labels with EU tolerance limits and guiding the choice of reference methods for further confirmation purposes. Graphical Abstract","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"89 - 99"},"PeriodicalIF":1.8,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47108682","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}
Pub Date : 2023-02-20DOI: 10.1177/09670335231156472
Kelly Torralvo, F. Durgante, C. Pasquini, W. Magnusson
In megadiverse regions, such as the Amazon, the identification of species generally requires specialists that are often not available. Therefore, the use of new species-recognition tools is necessary to streamline surveys and avoid errors in species identification that lead to ineffective decision-making. Near infrared spectroscopy is a quick and non-destructive tool that has been widely used in the recognition of biodiversity. In addition to being used as an indicator group, anurans have species with high morphological diversity, which make them the focus of studies and application of new tools that help in the identification and recognition at the species level. In this study, the viability of recognition of species of live Amazonian frogs under field conditions using the near infrared technique and portable equipment was examined. The performance of classification models based on a linear discriminant analysis, built using spectra obtained from the dorsal and ventral surfaces of four pairs of phylogenetically-close and morphologically-similar species was evaluated. It was possible to distinguish the species of live anurans in five of the eight species studied with hit rates above 80% when using only one spectral reading per individual. The overall mean of correct prediction of the models was below that of previous studies that tested the method with anurans, which are likely to be due to particularities in the acquisition of spectra under field conditions and live species. Therefore, suggestions are made to improve the predictive capacity of the techniques.
{"title":"Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field","authors":"Kelly Torralvo, F. Durgante, C. Pasquini, W. Magnusson","doi":"10.1177/09670335231156472","DOIUrl":"https://doi.org/10.1177/09670335231156472","url":null,"abstract":"In megadiverse regions, such as the Amazon, the identification of species generally requires specialists that are often not available. Therefore, the use of new species-recognition tools is necessary to streamline surveys and avoid errors in species identification that lead to ineffective decision-making. Near infrared spectroscopy is a quick and non-destructive tool that has been widely used in the recognition of biodiversity. In addition to being used as an indicator group, anurans have species with high morphological diversity, which make them the focus of studies and application of new tools that help in the identification and recognition at the species level. In this study, the viability of recognition of species of live Amazonian frogs under field conditions using the near infrared technique and portable equipment was examined. The performance of classification models based on a linear discriminant analysis, built using spectra obtained from the dorsal and ventral surfaces of four pairs of phylogenetically-close and morphologically-similar species was evaluated. It was possible to distinguish the species of live anurans in five of the eight species studied with hit rates above 80% when using only one spectral reading per individual. The overall mean of correct prediction of the models was below that of previous studies that tested the method with anurans, which are likely to be due to particularities in the acquisition of spectra under field conditions and live species. Therefore, suggestions are made to improve the predictive capacity of the techniques.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"80 - 88"},"PeriodicalIF":1.8,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45768422","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}
Pub Date : 2023-02-08DOI: 10.1177/09670335231153953
Han Liu, Hubin Liu, Yao Fang, Ning Zhang, Yuhui Yuan, Longlian Zhao, Junhui Li
Sorghum has a long history of cultivation and is an important food and economic crop. It can be divided into glutinous and non-glutinous varieties according to the starch structure and content. Rapid discrimination between the two would help the winemaking, feed, and food industries complete purchase pricing, ingredients, and quality control. In this study, 38 different samples were acquired, including 14 glutinous and 24 non-glutinous sorghum samples. Near infrared (NIR) spectra of glutinous and non-glutinous sorghum, pre-treated using the standard normal variable (SNV) transformation were found to have slightly different absorbances in the combination and first overtone bands. Based on the distribution of the starch-related and hydrogen-containing groups in the NIR region, it was concluded that glutinous sorghum has more C-O and C-C groups than non-glutinous sorghum. This study proposes an approach based on typical samples and direct calibration (TSDC) for binary discrimination. The TSDC approach consists of three functions. First, typical samples of two types of samples were selected. Second, typical type samples are used as dependent variables, predicted samples are used as independent variables, and formula regression is used to obtain fitted coefficients. Finally, if the formula regression model has no solution or the fitted coefficient is 1, typical type samples are reselected. Using the TSDC approach, discrimination accuracy can achieve 100% accuracy at 0.5 threshold. A larger threshold can be set to select better type characteristic predicted samples for discrimination. The TSDC approach can build excellent model through real relevance between the NIR spectra and the properties of interest, and the use of typical type samples greatly reduces modeling work compared with complex pattern recognition methods, especially for highly varied agricultural products. Therefore, it can efficiently propel the application and development of NIR detection technology. More research is required to apply the TSDC approach to three types of samples.
{"title":"Near infrared spectroscopy discriminates glutinous and non-glutinous sorghum using an approach based on typical samples and direct calibration","authors":"Han Liu, Hubin Liu, Yao Fang, Ning Zhang, Yuhui Yuan, Longlian Zhao, Junhui Li","doi":"10.1177/09670335231153953","DOIUrl":"https://doi.org/10.1177/09670335231153953","url":null,"abstract":"Sorghum has a long history of cultivation and is an important food and economic crop. It can be divided into glutinous and non-glutinous varieties according to the starch structure and content. Rapid discrimination between the two would help the winemaking, feed, and food industries complete purchase pricing, ingredients, and quality control. In this study, 38 different samples were acquired, including 14 glutinous and 24 non-glutinous sorghum samples. Near infrared (NIR) spectra of glutinous and non-glutinous sorghum, pre-treated using the standard normal variable (SNV) transformation were found to have slightly different absorbances in the combination and first overtone bands. Based on the distribution of the starch-related and hydrogen-containing groups in the NIR region, it was concluded that glutinous sorghum has more C-O and C-C groups than non-glutinous sorghum. This study proposes an approach based on typical samples and direct calibration (TSDC) for binary discrimination. The TSDC approach consists of three functions. First, typical samples of two types of samples were selected. Second, typical type samples are used as dependent variables, predicted samples are used as independent variables, and formula regression is used to obtain fitted coefficients. Finally, if the formula regression model has no solution or the fitted coefficient is 1, typical type samples are reselected. Using the TSDC approach, discrimination accuracy can achieve 100% accuracy at 0.5 threshold. A larger threshold can be set to select better type characteristic predicted samples for discrimination. The TSDC approach can build excellent model through real relevance between the NIR spectra and the properties of interest, and the use of typical type samples greatly reduces modeling work compared with complex pattern recognition methods, especially for highly varied agricultural products. Therefore, it can efficiently propel the application and development of NIR detection technology. More research is required to apply the TSDC approach to three types of samples.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"70 - 79"},"PeriodicalIF":1.8,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42301276","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}
This study explored the application of near infrared spectroscopy for quantitative and qualitative prediction of sulfur content in diesel fuel in the range of 10.3–1038.0 mg kg−1. The original spectra were preprocessed through various methods such as decentralization, normalization, multivariate scattering correction, and a smoothing (15-point window with second order polynomial fit). The performances of models based on partial least squares (PLS) regression, the bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling and Monte Carlo uninformative variable elimination algorithms in quantitative analysis of diesel samples were compared. The model for quantitative prediction of sulfur content in diesel samples using the BOSS-PLS algorithm had the highest performance and accuracy with a RMSEP of 36.20 mg kg−1 and r2 of 0.98 using a Savitzky-Golay second derivative. Diesel fuel samples were classified into five groups according to the sulfur content for qualitative analysis. The interval PLS method was then used to determine the characteristic spectra of the diesel samples. The experimental results indicated that the discriminant partial least squares qualitative analysis model had the highest performance with the characteristic spectrum from 12,493 to 10,892 cm−1, with 92.04% accuracy.
{"title":"Quantitative and qualitative prediction of sulfur content in diesel by near infrared spectroscopy","authors":"Q. Zheng, Hua Huang, Shiping Zhu, BaoHua Qi, Xin Tang","doi":"10.1177/09670335231153960","DOIUrl":"https://doi.org/10.1177/09670335231153960","url":null,"abstract":"This study explored the application of near infrared spectroscopy for quantitative and qualitative prediction of sulfur content in diesel fuel in the range of 10.3–1038.0 mg kg−1. The original spectra were preprocessed through various methods such as decentralization, normalization, multivariate scattering correction, and a smoothing (15-point window with second order polynomial fit). The performances of models based on partial least squares (PLS) regression, the bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling and Monte Carlo uninformative variable elimination algorithms in quantitative analysis of diesel samples were compared. The model for quantitative prediction of sulfur content in diesel samples using the BOSS-PLS algorithm had the highest performance and accuracy with a RMSEP of 36.20 mg kg−1 and r2 of 0.98 using a Savitzky-Golay second derivative. Diesel fuel samples were classified into five groups according to the sulfur content for qualitative analysis. The interval PLS method was then used to determine the characteristic spectra of the diesel samples. The experimental results indicated that the discriminant partial least squares qualitative analysis model had the highest performance with the characteristic spectrum from 12,493 to 10,892 cm−1, with 92.04% accuracy.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"63 - 69"},"PeriodicalIF":1.8,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44287943","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}
Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants.
{"title":"Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging","authors":"Songhao Li, Huilin Wu, Jing Zhao, Yu Liu, Yun Li, Houcheng Liu, Yiting Zhang, Yubin Lan, Xinglong Zhang, Yutao Liu, Yongbing Long","doi":"10.1177/09670335221148593","DOIUrl":"https://doi.org/10.1177/09670335221148593","url":null,"abstract":"Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"41 - 51"},"PeriodicalIF":1.8,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46164496","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}
Pub Date : 2023-01-16DOI: 10.1177/09670335221148594
M. Castillo, J. Acosta, G. Hodge, M. Vann, R. Lewis
Near infrared (NIR) spectroscopy calibration models were developed to predict chemical properties of flue-cured tobacco (Nicotiana tabacum L.) leaf samples using a microPHAZIRTM handheld NIR spectrometer. The sample data set consisted of 348 leaf-bundled samples of upper-stalk flue-cured tobacco leaves collected from an array of cultivars evaluated in multiple locations. Unprocessed leaf samples were intact whole unground leaves collected from curing barns. Processed leaf samples were further dried and ground before scanning. The NIR prediction models for percent reducing sugars, percent total alkaloids, and percent nicotine were very good for processed leaves [r2 (SEP in %) values = 0.98 (0.82), 0.92 (0.17), and 0.92 (0.14), respectively]. The models for the same three variables for unprocessed leaves were also very good, with only slightly lower fit statistics [r2 (SEP) = 0.93 (1.58), 0.87 (0.22), and 0.88 (0.18), respectively). Fit statistics for anabasine NIR models were intermediate with r2 (SEP in %) values ranging from 0.73 (0.003) to 0.76 (0.003), while the lowest fit statistics were observed for anatabine and norticotine with r2 (SEP in %) ranging from 0.49 (0.005) to 0.55 (0.017), respectively, for both unprocessed and processed leaves. Hence, use of a handheld NIR spectrometer would be of more limited value for these variables. The chemical composition of flue-cured tobacco leaf samples for some chemical traits can be directly assessed at the point when the leaves exit the curing barns, thus minimizing the need to dry and grind samples for colorimetric and chromatographic analyses.
采用microPHAZIRTM手持式近红外光谱仪建立了近红外光谱校准模型，用于预测烤烟叶片样品的化学性质。样本数据集包括348个上茎烤烟叶捆样本，这些样本来自多个地点的一系列品种。未加工的叶子样本是从烘烤仓收集的完整的未研磨的叶子。处理后的叶片样品在扫描前进一步干燥和研磨。还原糖百分比、总生物碱百分比和尼古丁百分比的近红外预测模型对加工后的叶片非常好[r2 (SEP in %)分别为0.98(0.82)、0.92(0.17)和0.92(0.14)]。对于未加工的叶片，同样三个变量的模型也非常好，只是拟合统计量略低[r2 (SEP)分别= 0.93(1.58)，0.87(0.22)和0.88(0.18)]。anatabine近红外模型的拟合统计量为中等，r2 (SEP in %)值为0.73(0.003)~ 0.76(0.003)，而anatabine和nortictine的拟合统计量最低，未加工和加工叶片的r2 (SEP in %)分别为0.49(0.005)~ 0.55(0.017)。因此，使用手持式近红外光谱仪对这些变量的价值更有限。烤烟烟叶样品的某些化学性状的化学成分可以在烟叶离开烤房时直接评估，从而最大限度地减少了干燥和研磨样品进行比色和色谱分析的需要。
{"title":"Analysis of alkaloids and reducing sugars in processed and unprocessed tobacco leaves using a handheld near infrared spectrometer","authors":"M. Castillo, J. Acosta, G. Hodge, M. Vann, R. Lewis","doi":"10.1177/09670335221148594","DOIUrl":"https://doi.org/10.1177/09670335221148594","url":null,"abstract":"Near infrared (NIR) spectroscopy calibration models were developed to predict chemical properties of flue-cured tobacco (Nicotiana tabacum L.) leaf samples using a microPHAZIRTM handheld NIR spectrometer. The sample data set consisted of 348 leaf-bundled samples of upper-stalk flue-cured tobacco leaves collected from an array of cultivars evaluated in multiple locations. Unprocessed leaf samples were intact whole unground leaves collected from curing barns. Processed leaf samples were further dried and ground before scanning. The NIR prediction models for percent reducing sugars, percent total alkaloids, and percent nicotine were very good for processed leaves [r2 (SEP in %) values = 0.98 (0.82), 0.92 (0.17), and 0.92 (0.14), respectively]. The models for the same three variables for unprocessed leaves were also very good, with only slightly lower fit statistics [r2 (SEP) = 0.93 (1.58), 0.87 (0.22), and 0.88 (0.18), respectively). Fit statistics for anabasine NIR models were intermediate with r2 (SEP in %) values ranging from 0.73 (0.003) to 0.76 (0.003), while the lowest fit statistics were observed for anatabine and norticotine with r2 (SEP in %) ranging from 0.49 (0.005) to 0.55 (0.017), respectively, for both unprocessed and processed leaves. Hence, use of a handheld NIR spectrometer would be of more limited value for these variables. The chemical composition of flue-cured tobacco leaf samples for some chemical traits can be directly assessed at the point when the leaves exit the curing barns, thus minimizing the need to dry and grind samples for colorimetric and chromatographic analyses.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"55 - 62"},"PeriodicalIF":1.8,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48272343","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}
Pub Date : 2022-12-13DOI: 10.1177/09670335221138955
M. K. Lakshmanan, B. Boelt, R. Gislum
This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.
{"title":"A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm","authors":"M. K. Lakshmanan, B. Boelt, R. Gislum","doi":"10.1177/09670335221138955","DOIUrl":"https://doi.org/10.1177/09670335221138955","url":null,"abstract":"This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"24 - 32"},"PeriodicalIF":1.8,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47554785","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}
Pub Date : 2022-11-30DOI: 10.1177/09670335221136545
Kittipon Aparatana, Yumika Naomasa, Morito Sano, Kenta Watanabe, Muneshi Mitsuoka, M. Ueno, Y. Kawamitsu, E. Taira
Sugar quality (Brix and Pol) is the key index to evaluate the value of sugarcane. Hence, a rapid, accurate, and time-efficient method is needed to determine the sugar quality. This study develops a two-point sugarcane quality model that uses a benchtop near infrared (NIR) spectrometer and a portable visible–near infrared (Vis-NIR) spectrometer to measure the sugarcane juice and stalk spectra, respectively. GT two experiments for developing a two-point sugarcane quality model. In the first, a model to calibrate the sugar quality as measured by a polarimeter and refractometer, and also by the benchtop NIR spectrometer. In the second, we developed a model to calibrate the sugar quality predicted from the calibration model developed in the first experiment, by measuring the sugarcane stalk absorption spectra using a portable Vis-NIR spectrometer. The results of the first experiment showed that the standard normal variate (SNV) spectral pretreatment was the most effective method for Brix calibration, with a coefficient of determination of prediction ( r p 2 ) of 0.99 and root mean square error of prediction (RMSEP) of 0.2%. In the case of Pol, second derivatives were the best spectral pretreatment for effective calibration (r2 = 0.99, RMSEP = 0.3%). The results of the second experiment showed that the multiple linear regression model developed using the stalk spectra with the second derivative was the best model for Brix calibration (r2 = 0.70, RMSEP = 1.4%). The second derivative with the SNV pretreatment was best for Pol calibration (r2 = 0.70, RMSEP = 1.4%). Our study showed that a sugar quality regression model can be developed for a portable Vis-NIR spectrometer using the data from the sugar quality predicted by a benchtop NIR spectrometer.
{"title":"Predicting sugarcane quality using a portable visible near infrared spectrometer and a benchtop near infrared spectrometer","authors":"Kittipon Aparatana, Yumika Naomasa, Morito Sano, Kenta Watanabe, Muneshi Mitsuoka, M. Ueno, Y. Kawamitsu, E. Taira","doi":"10.1177/09670335221136545","DOIUrl":"https://doi.org/10.1177/09670335221136545","url":null,"abstract":"Sugar quality (Brix and Pol) is the key index to evaluate the value of sugarcane. Hence, a rapid, accurate, and time-efficient method is needed to determine the sugar quality. This study develops a two-point sugarcane quality model that uses a benchtop near infrared (NIR) spectrometer and a portable visible–near infrared (Vis-NIR) spectrometer to measure the sugarcane juice and stalk spectra, respectively. GT two experiments for developing a two-point sugarcane quality model. In the first, a model to calibrate the sugar quality as measured by a polarimeter and refractometer, and also by the benchtop NIR spectrometer. In the second, we developed a model to calibrate the sugar quality predicted from the calibration model developed in the first experiment, by measuring the sugarcane stalk absorption spectra using a portable Vis-NIR spectrometer. The results of the first experiment showed that the standard normal variate (SNV) spectral pretreatment was the most effective method for Brix calibration, with a coefficient of determination of prediction ( r p 2 ) of 0.99 and root mean square error of prediction (RMSEP) of 0.2%. In the case of Pol, second derivatives were the best spectral pretreatment for effective calibration (r2 = 0.99, RMSEP = 0.3%). The results of the second experiment showed that the multiple linear regression model developed using the stalk spectra with the second derivative was the best model for Brix calibration (r2 = 0.70, RMSEP = 1.4%). The second derivative with the SNV pretreatment was best for Pol calibration (r2 = 0.70, RMSEP = 1.4%). Our study showed that a sugar quality regression model can be developed for a portable Vis-NIR spectrometer using the data from the sugar quality predicted by a benchtop NIR spectrometer.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"14 - 23"},"PeriodicalIF":1.8,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44162868","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}
Simple, rapid, and reliable determination of the biuret content in urea fertilizer is very important for the development of fertilizer industry. A near infrared diffuse reflectance measurement system with a portable spectrometer was developed, in which the reference and dark background spectrum could also be recorded automatically in addition to the absorbance data. The key performances of the proposed NIR system have been tested on urea fertilizer. Numerical experiments showed that the coefficient of determination (R2) of the external validation set was 0.97, with a root mean square error (RMSE) of 0.04%. The ratios of the performance deviation (RPD) value in the calibration and validation sets were 12 and 3.5, respectively. It can be concluded that this NIR system for the determination of biuret content in urea fertilizer may potentially be used as an alternative method to traditional wet chemical methods due to its simplicity, sensitivity, and portability.
{"title":"A diffuse reflectance portable near infrared spectroscopy system for the determination of biuret content in urea fertilizer","authors":"Jing Liu, Sha Yu, Shupeng Hu, Ziyang Ling, Jiguang Gao, Binmei Liu, Lixiang Yu, Yang Yang, Ye Yang, Qi Wang, Xiaoyu Ni, Liping Zhao, Yuejin Wu","doi":"10.1177/09670335221136546","DOIUrl":"https://doi.org/10.1177/09670335221136546","url":null,"abstract":"Simple, rapid, and reliable determination of the biuret content in urea fertilizer is very important for the development of fertilizer industry. A near infrared diffuse reflectance measurement system with a portable spectrometer was developed, in which the reference and dark background spectrum could also be recorded automatically in addition to the absorbance data. The key performances of the proposed NIR system have been tested on urea fertilizer. Numerical experiments showed that the coefficient of determination (R2) of the external validation set was 0.97, with a root mean square error (RMSE) of 0.04%. The ratios of the performance deviation (RPD) value in the calibration and validation sets were 12 and 3.5, respectively. It can be concluded that this NIR system for the determination of biuret content in urea fertilizer may potentially be used as an alternative method to traditional wet chemical methods due to its simplicity, sensitivity, and portability.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"33 - 40"},"PeriodicalIF":1.8,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43956830","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}
Pub Date : 2022-11-02DOI: 10.1177/09670335221134206
Alper Kılıç, Yun Miao, V. Koomson
Hemoglobin is one of the most important chromophores in the human body, since oxygen is carried to the tissue by binding with the hemoglobin. Therefore measuring the concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) is very important in both clinical settings and academic fields. Frequency domain near infrared spectroscopy (fdNIR spectroscopy) is a technique that can be used to measure the absolute concentrations of HbO and HbR non-invasively and locally. The fdNIR spectrometer utilizes the attenuation and the phase shift (with respect to the source) that an intensity modulated NIR light experiences in order to calculate the absorption (μa) and reduced scattering (μ′s) coefficient of the tissue. In this work, a miniaturized dual-wavelength fdNIR spectrometry instrument is presented with both tissue-like phantom and in vivo occlusion measurements. Systematic tests were performed on tissue phantoms to quantify the accuracy and stability of the instrument. The absolute errors for μa and μ′s were below 15% respectively. The amplitude and phase uncertainty were below 0.25% and 0.35°. In vivo measurements were also conducted to further validate the system.
{"title":"Design of a miniaturized frequency domain near infrared spectrometer with validation in solid phantoms and human tissue","authors":"Alper Kılıç, Yun Miao, V. Koomson","doi":"10.1177/09670335221134206","DOIUrl":"https://doi.org/10.1177/09670335221134206","url":null,"abstract":"Hemoglobin is one of the most important chromophores in the human body, since oxygen is carried to the tissue by binding with the hemoglobin. Therefore measuring the concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) is very important in both clinical settings and academic fields. Frequency domain near infrared spectroscopy (fdNIR spectroscopy) is a technique that can be used to measure the absolute concentrations of HbO and HbR non-invasively and locally. The fdNIR spectrometer utilizes the attenuation and the phase shift (with respect to the source) that an intensity modulated NIR light experiences in order to calculate the absorption (μa) and reduced scattering (μ′s) coefficient of the tissue. In this work, a miniaturized dual-wavelength fdNIR spectrometry instrument is presented with both tissue-like phantom and in vivo occlusion measurements. Systematic tests were performed on tissue phantoms to quantify the accuracy and stability of the instrument. The absolute errors for μa and μ′s were below 15% respectively. The amplitude and phase uncertainty were below 0.25% and 0.35°. In vivo measurements were also conducted to further validate the system.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"31 1","pages":"3 - 13"},"PeriodicalIF":1.8,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45327653","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}
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