Determination of Moisture, Starch, Protein, and Fat in Common Beans (Phaseolus vulgaris L.) by Near Infrared Spectroscopy

Abstract The presence of moisture, starch, protein, and fat was determined in common beans (Phaseolus vulgaris L.) by near infrared (NIR) spectroscopy without any previous sample pretreatment except grinding. A set of 96 samples was used to calibrate the instrument by modified partial least-squares regression. The following statistical results were achieved: standard error of calibration (SEC) = 0.31 and square correlation coefficient (R2) = 0.96 for moisture; SEC = 0.76 and R2 = 0.92 for starch; SEC = 0.39 and R2 = 0.98 for protein; and SEC = 0.14 and R2 = 0.80 for fat. To validate the calibration, a set of 25 bean samples was used. Standard errors of prediction were 0.39, 0.90, 0.56, and 0.13 for moisture, starch, protein, and fat, respectively, and R2 for the regression of measurements by the reference method versus NIR analysis were 0.94, 0.88, 0.94, and 0.74 for moisture, starch, protein, and fat, respectively. To compare the results obtained for all 4 components of the validation set by NIR spectroscopy with those obtained by the reference methods, linear regression and paired t tests were applied, and the methods did not give significantly different results, P = 0.05.

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Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽

10.18047/poljo.25.1.7 ◽

2019 ◽

Vol 25 (1) ◽

pp. 48-55

Author(s):

Marina Vranić ◽

Marko Petek ◽

Krešimir Bošnjak ◽

Boris Lazarević ◽

Klaudija Carović Stanko

Keyword(s):

Phaseolus Vulgaris ◽

Common Bean ◽

Near Infrared ◽

Model Development ◽

Nir Spectroscopy ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Potassium Oxide ◽

Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefﬁcients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

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Predicting the lignin H/G ratio of Pinus sylvestris L. wood samples by PLS-R models based on near-infrared spectroscopy

Holzforschung ◽

10.1515/hf-2019-0186 ◽

2020 ◽

Vol 74 (7) ◽

pp. 655-662 ◽

Cited By ~ 1

Author(s):

Ana Alves ◽

Rita Simões ◽

José Luís Lousada ◽

José Lima-Brito ◽

José Rodrigues

Keyword(s):

Pinus Sylvestris ◽

Near Infrared ◽

External Validation ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Predicted Values ◽

G Ratio

AbstractSoftwood lignin consists mainly of guaiacyl (G) units and low amounts of hydroxyphenyl (H) units. Even in a small percentage, the ratio of H to G (H/G) and the intraspecific variation are crucial wood lignin properties. Analytical pyrolysis (Py) was already successfully used as a reference method to develop a model based on near-infrared (NIR) spectroscopy for the determination of the H/G ratio on Pinus pinaster (Pnb) wood samples. The predicted values of the Pinus sylvestris (Psyl) samples by this model were well correlated (R = 0.91) with the reference data (Py), but with a bias that increased with increasing H/G ratio. Partial least squares regression (PLS-R) models were developed for the prediction of the H/G ratio, dedicated models for Psyl wood samples and common models based on both species (Pnb and Psyl). All the calibration models showed a high coefficient of determination and low errors. The coefficient of determination of the external validation of the dedicated models ranged from 0.92 to 0.96 and for the common models ranged from 0.83 to 0.93. However, the comparison of the predictive ability of the dedicated and common models using the Psyl external validation set showed almost identical predicted values.

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Near-Infrared Spectroscopy Analysis of Seed Coats of Common Beans (Phaseolus vulgaris L.): A Potential Tool for Breeding and Quality Evaluation

Journal of Agricultural and Food Chemistry ◽

10.1021/jf204110k ◽

2012 ◽

Vol 60 (3) ◽

pp. 706-712 ◽

Cited By ~ 24

Author(s):

Marçal Plans ◽

Joan Simó ◽

Francesc Casañas ◽

José Sabaté

Keyword(s):

Infrared Spectroscopy ◽

Phaseolus Vulgaris ◽

Near Infrared Spectroscopy ◽

Quality Evaluation ◽

Near Infrared ◽

Common Beans ◽

Phaseolus Vulgaris L ◽

Spectroscopy Analysis ◽

Seed Coats ◽

Potential Tool

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Estimating sensory properties of common beans (Phaseolus vulgaris L.) by near infrared spectroscopy

Food Research International ◽

10.1016/j.foodres.2013.12.003 ◽

2014 ◽

Vol 56 ◽

pp. 55-62 ◽

Cited By ~ 15

Author(s):

Marçal Plans ◽

Joan Simó ◽

Francesc Casañas ◽

Roser Romero del Castillo ◽

Luis E. Rodriguez-Saona ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Phaseolus Vulgaris ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Sensory Properties ◽

Common Beans ◽

Phaseolus Vulgaris L

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Prediction of Soil TN and TC at a Farm-Scale Using VIS-NIR Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.225-226.1258 ◽

2011 ◽

Vol 225-226 ◽

pp. 1258-1261

Author(s):

Hai Qing Yang ◽

Bo Yan Kuang ◽

Abdul M. Mouazen

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Cost Effective ◽

Total Carbon ◽

Least Squares Regression ◽

Farm Scale ◽

Validation Set ◽

Effective Models ◽

Leave One Out ◽

Spectral Ranges

Building cost-effective models is of academic and practical value for fast measurement of soil properties, especially at a farm-scale. The aim of this study is to build quantitative models for soil total nitrogen (TN) and total carbon (TC) using visible and near infrared (VIS-NIR) spectroscopy. Dried samples (n=122) collected from an experimental farm, at Silsoe, Bedfordshire, United Kingdom, were scanned from 350 to 2500 nm at 1-nm intervals. Samples were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was carried out based on different spectral ranges. Result shows that the best predictions (R2>0.90 and RPD>3.3) are achieved for TN using the VIS range (400-700nm) and for TC using the VIS-NIR range (400-2500nm). It is concluded that VIS-NIR spectroscopy coupled with PLSR can be adopted for the prediction of soil TN and TC at a farm-scale.

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Application of near-infrared spectroscopy to predict the cooking times of aged common beans (Phaseolus vulgaris L.)

Journal of Food Engineering ◽

10.1016/j.jfoodeng.2020.110056 ◽

2020 ◽

Vol 284 ◽

pp. 110056 ◽

Cited By ~ 1

Author(s):

Elizabeth Nakhungu Wafula ◽

Irene Njoki Wainaina ◽

Carolien Buvé ◽

Nghia-Do-Trong Nguyen ◽

Peter Kahenya Kinyanjui ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Phaseolus Vulgaris ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Common Beans ◽

Phaseolus Vulgaris L ◽

Cooking Times

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IMPACT OF BIO-FERTILIZER, MAGNETIZED WATER AND SALINITY ON COMMON BEANS (PHASEOLUS VULGARIS L.) PLANT

Menoufia Journal of Soil Science ◽

10.21608/mjss.2019.124985 ◽

2019 ◽

Vol 4 (5) ◽

pp. 313-314

Author(s):

M. N. E. Faiyad ◽

Fatma S. El- Shafei ◽

Manal F. Tantawy

Keyword(s):

Phaseolus Vulgaris ◽

Common Beans ◽

Phaseolus Vulgaris L ◽

Magnetized Water

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Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.141 ◽

1998 ◽

Vol 6 (1) ◽

pp. 229-234 ◽

Cited By ~ 27

Author(s):

William R. Windham ◽

W.H. Morrison

Keyword(s):

Fatty Acids ◽

Fatty Acid ◽

Near Infrared ◽

Fatty Acid Content ◽

Reference Method ◽

Nir Spectroscopy ◽

Coefficient Of Determination ◽

Longissimus Dorsi ◽

Near Infrared Reflectance ◽

Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

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