A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples

1989 ◽  
Vol 43 (6) ◽  
pp. 1045-1049 ◽  
Author(s):  
P. Robert ◽  
D. Bertrand ◽  
M. Crochon ◽  
J. Sabino
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Sugar Content ◽  
Principal Component ◽  
Base System ◽  
Mathematical Procedure ◽  
Analytical Applications ◽  
Spectral Libraries ◽  
Calibration Equations

Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.

Non-Destructive Determination of Sugar Content in Satsuma Mandarin Fruit by near Infrared Transmittance Spectroscopy

1995 ◽  
Vol 3 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Kumi Miyamoto ◽  
Yoshinobu Kitano
Keyword(s):  
Wavelength Range ◽  
Near Infrared ◽  
Sugar Content ◽  
Satsuma Mandarin ◽  
Infrared Transmittance ◽  
Calibration Samples ◽  
Sufficient Variation ◽  
Non Destructive ◽  
Calibration Equations

Using many samples of satsuma mandarins collected at random in packing houses, a method of establishing stable calibration equations to determine sugar content in the fruit by near infrared (NIR) transmittance spectroscopy was investigated. High accuracy in the determination of sugar content in the fruit could be obtained by multiple linear regression (MLR) using second derivative (D2) spectra in the 710–930 nm region. As a minimum, the four wavelengths mentioned below were needed as the predictor variables of the MLR equation. The D2 spectral change in the region of 900–910 nm was due to sugars (sucrose, glucose and fructose). A wavelength range of 880–890 nm and a wavelength range of 900–910 nm were selected as the best pair. A wavelength range of 740–755 nm region or 840–855 nm region compensated for the different optical pathlength of each fruit. One of the wavelengths around 794 nm or 835 nm was needed to compensate for the influence of fruit temperature. The influence of various factors such as fruit variety, growing location, harvest season and production year were investigated when the calibration was developed. When calibration samples had sufficient variation in fruit quality, it was possible to obtain a stable equation for all variations.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Determination of the moisture content of paprika (Capsicum annuum) powders: a comparative study/ Determinación de la humedad del pimentón (Capsicum annuum): estudio comparativo

1996 ◽  
Vol 2 (1) ◽  
pp. 23-27 ◽  
Author(s):  
E. Forgács ◽  
V. Kiss ◽  
T. Cserháti ◽  
J. Holló
Keyword(s):  
Principal Component Analysis ◽  
Moisture Content ◽  
Capsicum Annuum ◽  
Near Infrared ◽  
Principal Component ◽  
Peak Height ◽  
Data Matrix ◽  
Drying Method ◽  
Electric Oven

The moisture content of 25 different paprika powders was determined by an electronic moisture analyzer at 40, 50, 60, 70, 80, 90, 100 and 105°C by near infrared spectroscopy, using both peak area and peak height for the water; and by the traditional drying method, using an electric oven at 100°C for 1, 2 and 3 h. The data matrix was evaluated by principal component analysis. It was established that the moisture content of paprika powders can be equally determined by each method. The use of the NIR method has been proposed because it is both rapid and accurate, and the presence of other volatile compounds does not influence the reliability of the determination of the moisture content.

Lipid Analysis by near Infrared Spectroscopy to Evaluate Inhibitory Effects of Swine Waste Compost on Plant Growth

2008 ◽  
Vol 16 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Takayuki Fujiwara ◽  
Keiichi Murakami
Keyword(s):  
Plant Growth ◽  
Lipid Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Swine Waste ◽  
Inhibitory Effects ◽  
Absorption Bands ◽  
Freeze Dried ◽  
Calibration Equations

The lipid content of swine manure decreases during the process of composting, and inhibitory effects of compost on root growth in germination tests are strongly correlated to lipid content. Therefore, we tested whether the determination of the lipid content of swine waste compost by near infrared (NIR) spectroscopy provided a measure by which the degree of inhibition of plant growth by immature compost could be predicted. Reflectance spectra of untreated compost samples, as well as freeze-dried and milled samples, were taken using a scanning monochromator. Second derivative spectra from 700 nm to 2500 nm and multiple regression analysis were used to develop calibration equations for lipid content and moisture. A pronounced absorption peak of lipid was found at 2310 nm, attributable to the absorption bands of the CH2 stretching–bending combination. However, calibration equations containing this absorption band were inappropriate for lipid determination, because sawdust and rice husk, which were added to the compost, influenced the spectra in this band. The standard error of prediction ( SEP) of the best calibrations for lipids in dry and untreated samples was 6.0 g kg−1 and 3.2 g kg−1, while the ratios of the standard deviation and the range in the prediction set to SEP (RPD and RER) were 5.5 and 2.8, and 13.5 and 5.0, respectively. The main wavelengths of these calibration equations were 1700 nm for dry samples and 1764 nm for untreated samples, which were attributed to the absorption bands of the CH2 stretching first overtone. In conclusion, the determination of lipid content in dry compost samples by NIR spectroscopy provided an indirect estimate of the maturity of swine waste compost. Moreover, NIR spectroscopy was found useful for the rough assessment of the maturity of untreated swine waste compost.

Analysis of undried grass silage by near-infrared spectroscopy

1994 ◽  
Vol 1994 ◽  
pp. 119-119
Author(s):  
H W Morris ◽  
S Fisher ◽  
J R Newbold ◽  
S Wilson ◽  
C W Ashby ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Volatile Components ◽  
Chemical Methods ◽  
Grass Silage ◽  
Nutrient Value ◽  
Wet Chemical ◽  
Calibration Equations

The analysis of grass silage by near-infrared spectroscopy (NIR) of dried samples is established as a valid alternative to wet chemical methods. Analysis of undried samples offers potential advantages in terms of :d of analysis and accuracy of determination of volatile components, provided calibration equations can be validated against independent populations of silage. Accumulation of analyses for a large number of pies allows relationships between silage nutrient value and management factors such as additive use, which are poorly understood, to be examined.

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