Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy

The pet food industry is interested in performing fast analyses to control the nutritional quality of their products. This study assessed the feasibility of near-infrared spectroscopy to predict mineral content in extruded dry dog food. Mineral content in commercial dry dog food samples (n = 119) was quantified by inductively coupled plasma optical emission spectrometry and reflectance spectra (850–2500 nm) captured with FOSS NIRS DS2500 spectrometer. Calibration models were built using modified partial least square regression and leave-one-out cross-validation. The best prediction models were obtained for S (coefficient of determination; R2 = 0.89), K (R2 = 0.85), and Li (R2 = 0.74), followed by P, B, and Sr (R2 = 0.72 each). Only prediction models for S and K were adequate for screening purposes. This study supports that minerals are difficult to determine with NIRS if they are not associated with organic molecules.

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Prediction of Composition of Broiler Chicken Excreta Using Near-infrared Reflectance Spectroscopy

Journal of Agricultural and Marine Sciences [JAMS] ◽

10.24200/jams.vol10iss1pp13-19 ◽

2005 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

I. T. Kadim ◽

W. Al-Marzooqi ◽

O. Mahgoub ◽

K. Annamalai

Keyword(s):

Near Infrared ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Acid Detergent Fibre ◽

Gross Energy ◽

Calibration Equations

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for the prediction of the content of dry matter (DM); nitrogen (N), ether extract (EE), neutral detergent fibre (NDF), acid detergent fibre (ADF), gross energy (GE), calcium (Ca) and phosphate (P) in broiler excreta samples. The chemical composition of broiler excreta was determined by the conventional chemical analysis methods in the laboratory and compared with NIRS. Excreta samples (n = 72) were oven dried (60 oC) and analyzed for DM, N, EE, NDF, ADF, GE, Ca and P. The determined values (mean ± SD) were as follows: DM: 31.46 ± 7.65 (range:19.14 - 44.51), N: 5.85 ± 2.88 (range: 4.85 -7.00), EE: 1.37 ± 0.25 (range: 0.88-1.99), ADF: 16.71 ± 1.99 (range: 12.11-19.97), NDF: 26.26 ± 1.63 (range: 22.03-30.21), GE: 15.27 ± 0.33 (range: 14.52-16.11), Ca: 2.57 ± 0.22 (range: 2.16-3.01), P: 1.79 ± 0.15 (range: 1.41-2.11). The samples were then scanned in a NIRS model 5000 analyzer and the spectra obtained for each sample. Calibration equations and prediction values were developed for broiler excreta samples. The software used modified partial least square regression statistic, as it is most suitable for natural products. For broiler excreta samples, the coefficient of determination (R2) and the standard error of prediction (SEP) was DM = 0.97, 1.27, N = 0.95, 0.72, EE = 0.92, 0.07, ADF = 0.87, 0.78, NDF = 0.88, 0.72, GE = 0.89; 0.24, Ca = 0.96, 0.06, P = 0.93, 0.09, respectively. The results indicate that it is possible to calibrate NIRS to predict major constituents in broiler excreta samples.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.467 ◽

2005 ◽

Vol 13 (3) ◽

pp. 147-154 ◽

Cited By ~ 6

Author(s):

Wolfgang Becker ◽

Norbert Eisenreich

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Conditions ◽

Transmission Spectra ◽

Mean Square ◽

Score Plot ◽

Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

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Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

Journal of AOAC International ◽

10.1093/jaoac/78.3.802 ◽

1995 ◽

Vol 78 (3) ◽

pp. 802-806 ◽

Cited By ~ 46

Author(s):

José Louis Rodriguez-Otero ◽

Maria Hermida ◽

Alberto Cepeda

Keyword(s):

Near Infrared ◽

Independent Set ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sample Treatment ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

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Visible and near infrared reflectance spectroscopy for field-scale assessment of Stagnosols properties

Plant Soil and Environment ◽

10.17221/220/2018-pse ◽

2018 ◽

Vol 64 (No. 6) ◽

pp. 276-282 ◽

Cited By ~ 1

Author(s):

Šestak Ivana ◽

Mesić Milan ◽

Zgorelec Željka ◽

Perčin Aleksandra ◽

Stupnišek Ivan

Keyword(s):

Soil Ph ◽

Near Infrared ◽

Soil Depth ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Infrared Reflectance ◽

Field Scale ◽

Near Infrared Reflectance ◽

Vnir Spectroscopy

Spectral data contain information on soil organic and mineral composition, which can be useful for soil quality monitoring. The objective of research was to evaluate hyperspectral visible and near infrared reflectance (VNIR) spectroscopy for field-scale prediction of soil properties and assessment of factors affecting soil spectra. Two hundred soil samples taken from the experiment field (soil depth: 30 cm; sampling grid: 15 × 15 m) were scanned using portable spectroradiometer (350–1050 nm) to identify spectral differences of soil treated with ten different rates of mineral nitrogen (N) fertilizer (0–300 kg N/ha). Principal component analysis revealed distinction between higher- and lower-N level treatments conditioned by differences in soil pH, texture and soil organic matter (SOM) composition. Partial least square regression resulted in very strong correlation and low root mean square error (RMSE) between predicted and measured values for the calibration (C) and validation (V) dataset, respectively (SOM, %: RC2 = 0.75 and RV2 = 0.74; RMSEC = 0.334 and RMSEV = 0.346; soil pH: RC2 = 0.78 and RV2 = 0.62; RMSEC = 0.448 and RMSEV = 0.591). Results indicated that hyperspectral VNIR spectroscopy is an efficient method for measurement of soil functional attributes within precision farming framework.

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Transferability and scaling of soil total carbon prediction models in Florida

10.7287/peerj.preprints.494v1 ◽

2014 ◽

Author(s):

Sabine Grunwald ◽

Congrong Yu ◽

Xiong Xiong

Keyword(s):

Prediction Error ◽

Near Infrared ◽

Prediction Models ◽

Regional Scale ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Total Carbon ◽

Support Vector ◽

Domain Iii

The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model’s transferability among fields, and (iii) evaluate the up- and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up- and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.

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Nondestructive NIR Reflectance Spectroscopic Method for Rapid Fatty Acid Analysis of Peanut Seeds

Peanut Science ◽

10.3146/ps10-3.1 ◽

2011 ◽

Vol 38 (2) ◽

pp. 85-92 ◽

Cited By ~ 6

Author(s):

Jaya Sundaram ◽

Chari V. Kandala ◽

Christopher L. Butts ◽

Charles Y. Chen ◽

Victor Sobolev

Keyword(s):

Fatty Acids ◽

Fatty Acid ◽

Near Infrared ◽

Methyl Esters ◽

Spectroscopic Method ◽

Nondestructive Method ◽

Partial Least Square ◽

Least Square ◽

Coefficient Of Determination ◽

Near Infrared Reflectance

ABSTRACT Near Infrared Reflectance Spectroscopy (NIRS) was used to rapidly and nondestructively analyze the fatty acid concentration present in peanut seeds samples. Absorbance spectra were collected in the wavelength range from 400 nm to 2500 nm using NIRS. The oleic, linoleic and palmitic fatty acids were converted to their corresponding methyl esters and their concentrations were measured using a gas chromatograph (GC). Partial least square (PLS) analysis was performed on a calibration set, and models were developed for prediction of fatty acid concentrations. The best model was selected based on the coefficient of determination (R2), Root Mean Square Error of Prediction, residual percent deviation (RPD) and correlation coefficient percentage between the gas chromatography measured values and the NIR predicted values. The NIR reflectance model developed yielded RPD values of three and above for prediction of the three fatty acids, indicating that this nondestructive method would be suitable for fatty acid predictions in peanut seeds.

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Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS)

PeerJ ◽

10.7717/peerj.8619 ◽

2020 ◽

Vol 8 ◽

pp. e8619

Author(s):

Isadora Kaline Camelo Pires de Oliveira Galdino ◽

Hévila Oliveira Salles ◽

Karina Maria Olbrich dos Santos ◽

Germano Veras ◽

Flávia Carolina Alonso Buriti

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Proximate Composition ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Composition Analysis ◽

Reference Methods

Background In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky–Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R2cal and R2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

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Gross calorific and ash content assessment of recycled sawdust from mushroom cultivation using near infrared spectroscopy

MATEC Web of Conferences ◽

10.1051/matecconf/201819203021 ◽

2018 ◽

Vol 192 ◽

pp. 03021 ◽

Cited By ~ 1

Author(s):

Jetsada Posom ◽

Jirawat phuphanutada ◽

Ravipat Lapcharoensuk

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Ash Content ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mushroom Cultivation

The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.

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Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

Foods ◽

10.3390/foods10020317 ◽

2021 ◽

Vol 10 (2) ◽

pp. 317

Author(s):

Olga Escuredo ◽

María Shantal Rodríguez-Flores ◽

Laura Meno ◽

María Carmen Seijo

Keyword(s):

Physicochemical Properties ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Botanical Origin ◽

Linear Discriminant ◽

Physicochemical Composition ◽

Natural Foods

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

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