Pub Date : 2021-10-01DOI: 10.1177/09670335211035992
S. Treguier, C. Couderc, Marjorie Audonnet, Leïla Mzali, H. Tormo, M. Daveran-Mingot, Hicham Ferhout, D. Kleiber, C. Levasseur-Garcia
The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates.
{"title":"Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification","authors":"S. Treguier, C. Couderc, Marjorie Audonnet, Leïla Mzali, H. Tormo, M. Daveran-Mingot, Hicham Ferhout, D. Kleiber, C. Levasseur-Garcia","doi":"10.1177/09670335211035992","DOIUrl":"https://doi.org/10.1177/09670335211035992","url":null,"abstract":"The biological processes of interest to agro-industry involve numerous bacterial species. Lactic acid bacteria produce metabolites capable of fermenting food products and modifying their organoleptic properties, and plant-growth-promoting rhizobacteria can act as biofertilizers, biostimulants, or biocontrol agents in agriculture. The protocol of conventional techniques for bacterial identification, currently based on genotyping and phenotyping, require specific sample preparation and destruction. The work presented herein details a method for rapid identification of lactic acid bacteria and rhizobacteria at the genus and species level. To develop the method, bacteria were inoculated on an agar medium and analyzed by near infrared (NIR) and ultraviolet-visible-NIR (UV-Vis-NIR) spectroscopy. Artificial neural network models applied to the UV-Vis-NIR spectra correctly identified the genus (species) of 70% (63%) of the lactic acid bacteria and 67% of the rhizobacteria on an independent prediction set of unknown bacterial strains. These results demonstrate the potential of UV-Vis-NIR spectroscopy to identify bacteria directly on agar plates.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"18 4 1","pages":"278 - 288"},"PeriodicalIF":1.8,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65355208","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 : 2021-09-06DOI: 10.1177/09670335211042016
Zengling Yang, Linwei Cai, Lujia Han, Xia Fan, Xian Liu
After half a century of development, near infrared spectroscopy has now reached a relatively mature position. It is widely used in agriculture, food, petrochemical, and pharmaceutical fields and plays an increasingly important role in industrial and agricultural production and commercial trade. The development of Standards based on NIR spectroscopy represents the degree of recognition of the technology and applications. This article reviews domestic and global near infrared spectroscopy Standards in order to collate the myriad of existing standards in one listing.
{"title":"Review of standards for near infrared spectroscopy methods","authors":"Zengling Yang, Linwei Cai, Lujia Han, Xia Fan, Xian Liu","doi":"10.1177/09670335211042016","DOIUrl":"https://doi.org/10.1177/09670335211042016","url":null,"abstract":"After half a century of development, near infrared spectroscopy has now reached a relatively mature position. It is widely used in agriculture, food, petrochemical, and pharmaceutical fields and plays an increasingly important role in industrial and agricultural production and commercial trade. The development of Standards based on NIR spectroscopy represents the degree of recognition of the technology and applications. This article reviews domestic and global near infrared spectroscopy Standards in order to collate the myriad of existing standards in one listing.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"313 - 320"},"PeriodicalIF":1.8,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48174524","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 : 2021-08-01DOI: 10.1177/09670335221114746
K. M. Catunda, A. Churchill, S. Power, B. Moore
Near infrared reflectance (NIR) spectroscopy has been used by the agricultural industry as a rapid and inexpensive technique to quantify nutritional chemistry in plants. The aim of this study was to evaluate the performance of NIR calibrations in predicting the nutritional composition of ten pasture species that underpin livestock industries in many countries. The species comprised a range of functional diversity (C3 legumes; C3/C4 grasses; annuals/perennials) and origins (tropical/temperate; introduced/native) that grew under varied environmental conditions (control and experimentally induced warming and drought) over a period of more than two years (n = 2622). A maximal calibration set including 391 samples was used to develop and evaluate calibrations for all ten pasture species (global calibrations), as well as for subsets comprised of the plant functional groups. This study found that the global calibrations were appropriate to predict the six key nutritional quality parameters for the studied pasture species, with the highest estimation quality found for ash (ASH), crude protein (CP), amylase-treated neutral detergent fibre (aNDF) and acid detergent fibre (ADF), and the lowest for ether extract (EE) and acid detergent lignin (ADL) parameters. The plant functional group calibrations for C3 grasses performed better than the global calibrations for ASH, CP, ADF and EE parameters, whereas for C3 legumes and C4 grasses the functional group calibrations performed less well than the global calibrations for all nutritional parameters of these groups. Additionally, the calibrations were able to capture the range of variation in forage nutritional quality caused by future climate scenarios of warming and severe drought.
{"title":"Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups","authors":"K. M. Catunda, A. Churchill, S. Power, B. Moore","doi":"10.1177/09670335221114746","DOIUrl":"https://doi.org/10.1177/09670335221114746","url":null,"abstract":"Near infrared reflectance (NIR) spectroscopy has been used by the agricultural industry as a rapid and inexpensive technique to quantify nutritional chemistry in plants. The aim of this study was to evaluate the performance of NIR calibrations in predicting the nutritional composition of ten pasture species that underpin livestock industries in many countries. The species comprised a range of functional diversity (C3 legumes; C3/C4 grasses; annuals/perennials) and origins (tropical/temperate; introduced/native) that grew under varied environmental conditions (control and experimentally induced warming and drought) over a period of more than two years (n = 2622). A maximal calibration set including 391 samples was used to develop and evaluate calibrations for all ten pasture species (global calibrations), as well as for subsets comprised of the plant functional groups. This study found that the global calibrations were appropriate to predict the six key nutritional quality parameters for the studied pasture species, with the highest estimation quality found for ash (ASH), crude protein (CP), amylase-treated neutral detergent fibre (aNDF) and acid detergent fibre (ADF), and the lowest for ether extract (EE) and acid detergent lignin (ADL) parameters. The plant functional group calibrations for C3 grasses performed better than the global calibrations for ASH, CP, ADF and EE parameters, whereas for C3 legumes and C4 grasses the functional group calibrations performed less well than the global calibrations for all nutritional parameters of these groups. Additionally, the calibrations were able to capture the range of variation in forage nutritional quality caused by future climate scenarios of warming and severe drought.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"254 - 263"},"PeriodicalIF":1.8,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45815487","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 : 2021-07-08DOI: 10.1177/09670335211018759
Dong Sun, J. Cruz, M. Alcalà, R. Romero del Castillo, Silvia Sans, J. Casals
Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r2 values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r2 values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality.
{"title":"Near infrared spectroscopy determination of chemical and sensory properties in tomato","authors":"Dong Sun, J. Cruz, M. Alcalà, R. Romero del Castillo, Silvia Sans, J. Casals","doi":"10.1177/09670335211018759","DOIUrl":"https://doi.org/10.1177/09670335211018759","url":null,"abstract":"Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r2 values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r2 values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"289 - 300"},"PeriodicalIF":1.8,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/09670335211018759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46791616","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 : 2021-07-08DOI: 10.1177/09670335211025649
W. A. Whittier, G. Hodge, Juan López, C. Saravitz, J. Acosta
Due to a combination of durability, strength, and aesthetically pleasing color, teak (Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.
{"title":"The use of near infrared spectroscopy to predict foliar nutrient levels of hydroponically grown teak seedlings","authors":"W. A. Whittier, G. Hodge, Juan López, C. Saravitz, J. Acosta","doi":"10.1177/09670335211025649","DOIUrl":"https://doi.org/10.1177/09670335211025649","url":null,"abstract":"Due to a combination of durability, strength, and aesthetically pleasing color, teak (Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"301 - 310"},"PeriodicalIF":1.8,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/09670335211025649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49043977","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 : 2021-05-06DOI: 10.1177/09670335211006532
S. Treguier, Kévin Jacq, C. Couderc, Hicham Ferhout, H. Tormo, D. Kleiber, C. Levasseur-Garcia
Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500–3680 cm−1 range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790–3680 cm−1 spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.
{"title":"A method for highlighting differences between bacteria grown on nutrient agar using near infrared spectroscopy and principal component analysis","authors":"S. Treguier, Kévin Jacq, C. Couderc, Hicham Ferhout, H. Tormo, D. Kleiber, C. Levasseur-Garcia","doi":"10.1177/09670335211006532","DOIUrl":"https://doi.org/10.1177/09670335211006532","url":null,"abstract":"Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500–3680 cm−1 range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790–3680 cm−1 spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"269 - 277"},"PeriodicalIF":1.8,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/09670335211006532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41838007","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 : 2021-04-28DOI: 10.1177/0967033520979425
M. Bilal, Xiaobo Zou, M. Arslan, H. E. Tahir, Yue Sun, R. Aadil
In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples including, amongst others, total phenolic content, total flavanoid content and total antioxidant capacity. The developed models were assessed using coefficients of determination for the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The R2 for calibration and r2 for prediction varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.
{"title":"Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)","authors":"M. Bilal, Xiaobo Zou, M. Arslan, H. E. Tahir, Yue Sun, R. Aadil","doi":"10.1177/0967033520979425","DOIUrl":"https://doi.org/10.1177/0967033520979425","url":null,"abstract":"In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples including, amongst others, total phenolic content, total flavanoid content and total antioxidant capacity. The developed models were assessed using coefficients of determination for the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The R2 for calibration and r2 for prediction varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"191 - 200"},"PeriodicalIF":1.8,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0967033520979425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46500275","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 : 2021-04-28DOI: 10.1177/09670335211006526
J. Nantongo, Bradley M. Potts, T. Rodemann, H. Fitzgerald, Noel W. Davies, Julianne M. O’Reilly-Wapstra
Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.
{"title":"Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don)","authors":"J. Nantongo, Bradley M. Potts, T. Rodemann, H. Fitzgerald, Noel W. Davies, Julianne M. O’Reilly-Wapstra","doi":"10.1177/09670335211006526","DOIUrl":"https://doi.org/10.1177/09670335211006526","url":null,"abstract":"Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"245 - 256"},"PeriodicalIF":1.8,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/09670335211006526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48272074","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 : 2021-04-28DOI: 10.1177/0967033521999116
N. Bourne, D. Blyth, C. Simon
Ensuring aquafeeds meet the expected nutritional and physical specifications for a species is paramount in research and for the industry. This study aimed to examine the feasibility of predicting the proximate composition and starch gelatinisation (or cook) of aquaculture feeds (aquafeeds) regardless of their intended target species by near infrared (NIR) spectroscopy. Aquafeed samples used for nutrition experiments on various aquatic species with different nutritional requirements, as well as aquafeeds manufactured under varying extrusion conditions and steaming time to generate variable starch cook were used in this study. The various size pellets were ground before scanning by NIR spectroscopy, then models were developed to estimate dry matter, ash, total lipid, crude protein, and gross energy as well as starch cook. Proximate prediction models were successfully produced for diets with R2 values between 0.88 and 0.97 (standard error of cross-validation (SECV) 0.43 to 1.46, residual predictive deviation (RPD) 4.6 to 15.6), while starch cook models were produced with R2 values between 0.91 and 0.97 (SECV 3.60 to 5.76, RPD 1.2 to 1.9). The developed NIR models allow rapid monitoring of the nutritional composition, as well as starch cook, one of the major physical properties of aquafeeds. Models that provide rapid quality control assessment of diet characteristics is highly desirable in aquaculture research and the aquafeed industry.
{"title":"Rapid prediction of chemical composition and degree of starch cook of multi-species aquafeeds by near infrared spectroscopy","authors":"N. Bourne, D. Blyth, C. Simon","doi":"10.1177/0967033521999116","DOIUrl":"https://doi.org/10.1177/0967033521999116","url":null,"abstract":"Ensuring aquafeeds meet the expected nutritional and physical specifications for a species is paramount in research and for the industry. This study aimed to examine the feasibility of predicting the proximate composition and starch gelatinisation (or cook) of aquaculture feeds (aquafeeds) regardless of their intended target species by near infrared (NIR) spectroscopy. Aquafeed samples used for nutrition experiments on various aquatic species with different nutritional requirements, as well as aquafeeds manufactured under varying extrusion conditions and steaming time to generate variable starch cook were used in this study. The various size pellets were ground before scanning by NIR spectroscopy, then models were developed to estimate dry matter, ash, total lipid, crude protein, and gross energy as well as starch cook. Proximate prediction models were successfully produced for diets with R2 values between 0.88 and 0.97 (standard error of cross-validation (SECV) 0.43 to 1.46, residual predictive deviation (RPD) 4.6 to 15.6), while starch cook models were produced with R2 values between 0.91 and 0.97 (SECV 3.60 to 5.76, RPD 1.2 to 1.9). The developed NIR models allow rapid monitoring of the nutritional composition, as well as starch cook, one of the major physical properties of aquafeeds. Models that provide rapid quality control assessment of diet characteristics is highly desirable in aquaculture research and the aquafeed industry.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"216 - 225"},"PeriodicalIF":1.8,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0967033521999116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43831672","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 : 2021-04-25DOI: 10.1177/09670335211007543
Fabien Gogé, L. Thuriès, Y. Fouad, N. Damay, F. Davrieux, G. Moussard, Caroline Le Roux, Séverine Trupin-Maudemain, M. Valé, T. Morvan
Determining the chemical composition of animal manure rapidly is essential to manage fertilisation and decrease environmental pollution. Near infrared (NIR) spectroscopy is a non-destructive, inexpensive and rapid method to determine several components of manure simultaneously. This study investigated the ability of NIR spectroscopy to analyse the dry matter, total and ammonium nitrogen, phosphorus, calcium, potassium and magnesium contents in a database of heterogeneous cattle and poultry solid manures. The accuracy of calibration models obtained from different sample preparation methods (dried ground vs. fresh homogenized) and multivariate regression methods (partial least squares vs. local regression) were compared. The results showed that using local regression with NIR spectra of fresh homogenized manure could predict dry matter (R2=0.99, RMSEV = 1.64%, RPD = 13.31), total (R2=0.98, RMSEV = 0.16%, RPD = 7.11) and ammonium nitrogen (R2=0.97, RMSEV = 0.042%, RPD = 5.57) and phosphorus (R2=0.95, RMSEV = 0.10%, RPD = 5.56) contents accurately.
{"title":"Performance of near infrared spectroscopy of a solid cattle and poultry manure database depends on the sample preparation and regression method used","authors":"Fabien Gogé, L. Thuriès, Y. Fouad, N. Damay, F. Davrieux, G. Moussard, Caroline Le Roux, Séverine Trupin-Maudemain, M. Valé, T. Morvan","doi":"10.1177/09670335211007543","DOIUrl":"https://doi.org/10.1177/09670335211007543","url":null,"abstract":"Determining the chemical composition of animal manure rapidly is essential to manage fertilisation and decrease environmental pollution. Near infrared (NIR) spectroscopy is a non-destructive, inexpensive and rapid method to determine several components of manure simultaneously. This study investigated the ability of NIR spectroscopy to analyse the dry matter, total and ammonium nitrogen, phosphorus, calcium, potassium and magnesium contents in a database of heterogeneous cattle and poultry solid manures. The accuracy of calibration models obtained from different sample preparation methods (dried ground vs. fresh homogenized) and multivariate regression methods (partial least squares vs. local regression) were compared. The results showed that using local regression with NIR spectra of fresh homogenized manure could predict dry matter (R2=0.99, RMSEV = 1.64%, RPD = 13.31), total (R2=0.98, RMSEV = 0.16%, RPD = 7.11) and ammonium nitrogen (R2=0.97, RMSEV = 0.042%, RPD = 5.57) and phosphorus (R2=0.95, RMSEV = 0.10%, RPD = 5.56) contents accurately.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"29 1","pages":"226 - 235"},"PeriodicalIF":1.8,"publicationDate":"2021-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/09670335211007543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42652034","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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