Near Infrared Reflectance Spectroscopy in the Prediction of Sensory Properties of Beef

1995 ◽  
Vol 3 (2) ◽  
pp. 81-87 ◽  
Author(s):  
K.I. Hildrum ◽  
T. Isaksson ◽  
T. Næs ◽  
B.N. Nilsen ◽  
M. Rødbotten ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Multivariate Correlation ◽  
Heat Treated ◽  
Shear Press ◽  
Reflectance Measurements

Near infrared (NIR) spectroscopy in the prediction of sensory hardness, tenderness and juiciness of bovine M. Longissimus dorsi muscles has been studied. Principal component regressions (PCR) of sensory variables from NIR reflectance measurements on frozen/thawed beef of 120 heat treated samples yielded multivariate correlation coefficients of cross-validation of 0.74, 0.70 and 0.61 for hardness, tenderness and juiciness, respectively. The corresponding correlation coefficients for NIR measurements of fresh (non-frozen) samples were approximately 0.1 units lower for all sensory variables. Predicting Warner Bratzler (WB) shear press values from NIR measurements gave a correlation coefficient similar to that for prediction of sensory hardness. The univariate correlation coefficient between sensory hardness and WB shear press values was 0.90.

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

2021 ◽  
pp. 000370282199213
Author(s):  
Eshetu Bobasa ◽  
Michael Netzel ◽  
Anh Dao Thi Phan ◽  
Heather Smyth ◽  
Yasmina Sultanbawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Potential Health ◽  
Wide Range ◽  
Fruit Samples ◽  
The Fourier Transform ◽  
Kakadu Plum

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum ( Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

Near Infrared Reflectance Spectroscopic Determination of Protein Nitrogen in Plant Tissue

1984 ◽  
Vol 67 (3) ◽  
pp. 506-509
Author(s):  
Robert A Isaac ◽  
William C Johnson
Keyword(s):  
Plant Tissue ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Leaf Tissue ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Protein Nitrogen

Abstract A rapid, nondestructive method is described for the determination of protein nitrogen in plant tissue, using near infrared reflectance (NIR) spectroscopy. Procedures for instrument calibration are discussed. Comparisons between Kjeldahl nitrogen and NIR nitrogen are made for corn leaf tissue from Georgia and Indiana. Multiple correlation coefficients for other plant tissues such as peanuts, soybean, wheat, pecan, bermuda grass, and bent grass are also shown.

scholarly journals Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

2018 ◽  
Vol 11 (02) ◽  
pp. 1850005 ◽  
Author(s):  
Lijun Yao ◽  
Weiqun Xu ◽  
Tao Pan ◽  
Jiemei Chen
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Large Population ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Moving Window ◽  
Linear Discriminant ◽  
Transgenic Sugarcane ◽  
Polynomial Formula

The moving-window bis-correlation coefficients (MW-BiCC) was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and [Formula: see text]-thalassemia with visible and near-infrared (Vis–NIR) spectroscopy. The well-performed moving-window principal component analysis linear discriminant analysis (MW-PCA–LDA) was also conducted for comparison. A total of 306 transgenic (positive) and 150 nontransgenic (negative) leave samples of sugarcane were collected and divided to calibration, prediction, and validation. The diffuse reflection spectra were corrected using Savitzky–Golay (SG) smoothing with first-order derivative ([Formula: see text]), third-degree polynomial ([Formula: see text]) and 25 smoothing points ([Formula: see text]). The selected waveband was 736–1054[Formula: see text]nm with MW-BiCC, and the positive and negative validation recognition rates ([Formula: see text]_REC[Formula: see text], [Formula: see text]_REC[Formula: see text] were 100%, 98.0%, which achieved the same effect as MW-PCA–LDA. Another example, the 93 [Formula: see text]-thalassemia (positive) and 148 nonthalassemia (negative) of human hemolytic samples were collected. The transmission spectra were corrected using SG smoothing with [Formula: see text], [Formula: see text] and [Formula: see text]. Using MW-BiCC, many best wavebands were selected (e.g., 1116–1146, 1794–1848 and 2284–2342[Formula: see text]nm). The [Formula: see text]_REC[Formula: see text] and [Formula: see text]_REC[Formula: see text] were both 100%, which achieved the same effect as MW-PCA–LDA. Importantly, the BiCC only required calculating correlation coefficients between the spectrum of prediction sample and the average spectra of two types of calibration samples. Thus, BiCC was very simple in algorithm, and expected to obtain more applications. The results first confirmed the feasibility of distinguishing [Formula: see text]-thalassemia and normal control samples by NIR spectroscopy, and provided a promising simple tool for large population thalassemia screening.

scholarly journals Analysis of amino acid composition of sheep colostrum by near-infrared spectroscopy

2011 ◽  
Vol 49 (No. 5) ◽  
pp. 177-182 ◽  
Author(s):  
S. Kráčmar ◽  
R. Jankovská ◽  
K. Šustová ◽  
J. Kuchtík ◽  
L. Zeman
Keyword(s):  
Infrared Spectroscopy ◽  
Amino Acid ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Near Infrared Reflectance

This paper deals with changes in the basic composition of sheep colostrum within the first 72 hours after parturition on the one hand and with the possibility of determining the major components of sheep colostrum by near-infrared spectroscopy on the other. Levels of essential, nonessential and total amino acids in sheep colostrum were determined by near-infrared reflectance spectroscopy (NIRS). ). For each component, sets of 90 samples were used to calibrate the instrument by means of a modified partial least-squares regression. The values of correlation coefficients (r) were as follows: 0.979 for Thr; 0.954 for Val; 0.968 for Leu; 0.918 for Ile; 0.946 for Lys; 0.908 for Arg; 0.845 for His; 0.999 for Trp; 0.915 for Phe; 0.909 for Met; 0.939 for Cys; 0.911 for Σmet + Cys; 0.933 for Tyr; 0.945 for Asp; 0.935 for Glu; 0.986 for Ser; 0.985 for Pro; 0.957 for Gly; 0.949 for Ala; 0.940 for ΣEAA; 0.958 for ΣNEAA and 0.977 for ΣAA. Partial least-squares (PLS) regression was used to develop calibration models for examined samples of sheep colostrum. When using the NIRS method, the following correlation coefficients were calculated: Thr (0.959), Val (0.912), Leu (0.936), Ile (0.855), Lys (0.903), Arg (0.853), His (0.717), Trp (0.667), Phe (0.854), Met (0.867), Cys (0.895), Σmet + Cys (0.868), Tyr (0.886), Asp (0.910), Glu (0.882), Ser (0.968), Pro (0.968), Gly (0.923), Ala (0.916), ΣEAA (0.901), ΣNEAA (0.923) and ΣAA (0.943). Calibration was tested using the same set of samples.NIRS results were compared with reference data and no significant differences between them were found (P = 0.05). Calibration and validation models were constructed in the same way.Results of this study indicate that NIR spectroscopy can be used for a rapid analysis of amino acid contents in sheep colostrum.  

Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle

1996 ◽  
Vol 4 (1) ◽  
pp. 213-223 ◽  
Author(s):  
D. Cozzolino ◽  
I. Murray ◽  
R. Paterson ◽  
J.R. Scaife
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Thigh Muscle ◽  
Chicken Breast ◽  
Near Infrared Reflectance ◽  
Multivariate Correlation ◽  
Intact Muscle ◽  
Standard Normal ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.

Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

2013 ◽  
Vol 710 ◽  
pp. 768-771 ◽  
Author(s):  
Xiao Hong Wu ◽  
Tong Xiang Cai ◽  
Bin Wu ◽  
Jun Sun
Keyword(s):  
Clustering Algorithm ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Simple Computation ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Possibilistic Clustering ◽  
Discriminant Information

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

Strategies for Constructing Near-Infrared Spectral Libraries for the Identification of Drug Substances

1997 ◽  
Vol 51 (10) ◽  
pp. 1504-1510 ◽  
Author(s):  
Christina I. Gerhäusser ◽  
Karl-Artur Kovar
Keyword(s):  
Correlation Coefficient ◽  
Near Infrared ◽  
Full Range ◽  
Principal Component ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Derivative Spectra ◽  
Pattern Recognition Methods ◽  
Drug Substances ◽  
Second Derivative Spectra

The suitability of near-infrared reflectance analysis (NIRA) as a noninvasive method for “real-time” spectral verification of drug substances is evaluated. Strategies for development and optimization of reference libraries by different mathematical means are discussed. In order to compare data preprocessing techniques, factor-based libraries were constructed with the use of raw and second-derivative spectra of 17 benzodiazepines collected over the wavelength range from 1100 to 2500 nm. Validation of the models by predicting the identity of test set samples using the correlation coefficient proved that using the derivatives enhanced the library's selectivity dramatically, as the recognition rates increased from 25 to 100%. Two pattern recognition methods, correlation coefficient and distance, were applied to confirm the identity of 117 drugs by using libraries based on full-range second-derivative spectra. Recognition rates of 99.2% were obtained from a factor-based library with the use of the correlation coefficient. Even structural analogues could be reliably classified among highly dissimilar drugs. The construction of sub-libraries consisting solely of similar drugs offered no advantages. Considerable data reduction by principal component analysis (PCA) made NIRA a rapid and effective method of analysis providing a high degree of reliability.

scholarly journals Feasibility of Application of Near Infrared Reflectance (NIR) Spectroscopy for the Prediction of the Chemical Composition of Traditional Sausages

2021 ◽  
Vol 11 (23) ◽  
pp. 11282
Author(s):  
Eleni Kasapidou ◽  
Vasileios Papadopoulos ◽  
Paraskevi Mitlianga
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
External Validation ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Standard Errors ◽  
Calibration Model ◽  
Good Precision ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

In the present study, the potential of application of near infrared reflectance (NIR) spectroscopy for the estimation of the chemical composition of traditional (village style) sausages was examined. The chemical composition (moisture, ash, protein and, fat) was determined by standard reference methods. For the development of the calibration model, 39 samples of traditional fresh sausages were used, while for external validation, 10 samples of sausages were used. The correlation coefficients of calibration (RMSEC) and standard errors (SEC) were 0.92 and 1.58 (moisture), 0.77 and 0.18 (ash), 0.87 and 0.89 (protein) and 0.93 and 1.73 (fat). The cross-validation correlation coefficients (RMSECV) and standard errors (SECV) were 0.86 and 2.13 (moisture), 0.56 and 0.26 (ash), 0.78 and 1.17 (protein), and 0.88 and 2.17 (fat). The results of the calibration model showed that NIR spectroscopy can be applied to estimate with very good precision the fat content of traditional village-style sausages, whereas moisture and protein content can be estimated with good accuracy. The external validation confirmed the ability of NIR spectroscopy to predict the chemical composition of sausages.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
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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