Meat Science 63 (2003) 441–450 www.elsevier.com/locate/meatsci
Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS) D. Alomara,*, C. Gallob, M. Castan˜edab, R. Fuchslochera a Instituto de Produccio´n Animal, Facultad Ciencias Agrarias, Universidad Austral de Chile, PO Box 567, Valdivia, Chile Instituto de Ciencia y Tecnologı´a de Carnes, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, PO Box 567, Valdivia, Chile
b
Received 31 July 2000; received in revised form 29 April 2002; accepted 29 April 2002
Abstract Near infrared reflectance spectroscopy (NIRS) was evaluated as a tool to segregate different types of bovine meat and predict several chemical fractions on samples from two breeds, three muscles and six grading (Chilean system) categories. Samples previously minced, frozen and thawed, were scanned (400–2500 nm) and then analyzed for dry matter, crude protein, ether extract, total ash and collagen content, after freeze drying. Discriminant analysis using a partial least squares regression technique and cross validation, correctly identified breed and muscle type for most samples, but carcass grades, with the exception of samples from calves, were not successfully predicted. Best calibrations for chemical composition tested by cross-validation, showed R2 and standard errors of cross validation of 0.77 and 0.58% (dry matter), 0.82 and 0.48% (crude protein), 0.82 and 0.44% (ether extract). Calibrations for total ash showed a poor, and for collagen, a very poor prediction ability. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Meat; Beef composition; NIRS; Discriminant analysis
1. Introduction According to the Chilean standard for the grading of beef [Instituto Nacional de Normalizacio´n (INN), 1993], cattle carcasses should be classed by sex, age and fatness in six categories, identified by the letters V,A,C,U,N,O. Carcasses graded as ‘‘V’’ belong to younger (albeit older than calves) and lighter animals with ideal fat cover; those classified as ‘‘A’’, ‘‘C’’ and ‘‘U’’ represent carcasses from animals of increasing age up to category ‘‘N’’, obtained from older (heavier) animals irrespective of the degree of fatness. ‘‘O’’ is a special category where only calves are included (Gallo, Caro, Villarroel, & Araya, 1999). Fat content is an important trait for consumers, which shows a high degree of variability among grading categories for a given breed, and even within the same category when comparing carcasses of different breeds (Vidal, Gallo, & Gasic, 1998). Although the chemical analysis of meat, in terms of protein and fat, could allow the consumers to be aware of the quality of available food; time, cost and labour * Corresponding author. Fax: +56-63-221653. E-mail address:
[email protected] (D. Alomar).
are deterrent factors for the adoption of routine techniques of analysis. Important reductions in time and efforts spent in sample processing could be accomplished if faster methods could be established for routine measurements on fresh meat. Near infrared spectroscopy (NIRS) has shown promise as a rapid and effective tool for predicting meat composition of different animal species, either at the laboratory (Kruggel, Field, Riley, Radloff, & Horton, 1981; Lanza, 1983) or at on-line determinations (Isaksson, Nilsen, Togersen, Hammond, & Hildrum, 1996; Togersen, Isaksson, Nielsen, Baker, & Hildrum, 1999) using reflectance, transmittance or fiber optic technology (Mitsumoto, Maeda, Mitsuhashi, & Ozawa, 1991). Although NIRS analysis is an empirical method requiring reference methods of conventional ‘‘wet chemistry’’ in order to first develop the necessitated calibrations and second to establish periodical control checks (Davies & Grant, 1987), it could provide rapid and accurate results, often in conditions unsuitable for chemical analysis, such as on-line analysis at industrial scale (Togersen et al., 1999). NIRS has been also used in qualitative analysis as a discriminant tool serving to the identification and authentication of foods (Downey, 1996), for species identification in meats (Ding & Xu, 1999; McElhinney,
0309-1740/03/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved. PII: S0309-1740(02)00101-8
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Downey, & Fearn, 1999) and detecting adulteration of fish meal (Murray, Aucott, & Pike, 2001). A rapid and reliable technique to identify a particular type of meat should be useful, as some meats (species, cuts, grades) are more valuable for the consumers than others. If such a method could provide an authentication tool in order to prevent fraud, as well as to estimate chemical composition with acceptable accuracy and even to detect handling aspects (freezing, thawing) of the product, it could be a very helpful mechanism for the industry. NIRS could be a suitable technique for fulfilling all the above demands. Consequently, the objective of this work was to evaluate NIRS as a tool to discriminate muscle type and grading category according to the Chilean standards for beef carcasses. Complementary, breed identification between Friesian and Hereford meat was also examined. As a secondary objective, calibrations were tested for the prediction of chemical composition of raw beef meat.
2. Experimental 2.1. Samples Meat samples were obtained from carcasses with similar fat cover of castrated Friesian and Hereford males of each of the six categories (n=25, 17, 21, 25, 21 and 18 samples, for classes V, A, C, U, N and O, respectively) of the Chilean standard for the grading of beef, at a local abattoir in Valdivia, after 48 h at 0–4 C. Samples of ca. 200 g each, were abscised from three muscles representing common retail cuts in the local market: Longissimus thoracis et lumborum, a portion was dissected corresponding to the first 5 cm measured backwards from the 10th rib, from 22 Herefords and 23 Friesians; Supraspinosus, from 24 Herefords and 20 Friesians and Semitendinosus, from 20 Herefords and 18 Friesians, were both cross sectioned at each end. In all, a total of 127 samples was collected. Samples were prepared by eliminating the borders and any evidence of intermuscular fat, and subsequently ground in a food processor (Moulinex 1231), thoroughly homogenized by hand, sealed in plastic bags and frozen at ca. 15 C until processed. 2.2. Spectra Samples were thawed overnight in a refrigerator and then left for an hour at ambient temperature before being scanned. Spectra were taken by reflectance in a NIRSystems 65001 monochromator (Foss NIRSystems, Slver Spring, MD, USA) with a transport module and samples (ca. 150 g) placed in a couvette of 16.53.5 cm with a quartz window (Sample Cell NR-7080). Thirty readings per sample were taken between 400 and
2500 nm, every 2 nm. Optical density was stored as log (1/R), where R is the reflectance energy recovered by four lead sulphide detectors positioned at 45 angles from the sample. A personal computer with the software NIRS 31 (ISI, 1992) was utilized for the operation of the spectrometer, and to store and manage optical data. Calibrations were developed with the software WinISI II, Version 1.02 A, from Infrasoft International (ISI, 1999). 2.3. Discriminant equations for muscles and grading classes In order to identify muscle type, discriminant equations were developed (software WinISI II) using a partial least squares (PLS) regression technique. Separate spectra files, one for each muscle, were entered to be discriminated. In this method, a calibration matrix is set up with all samples by creating ‘‘dummy variables’’, assigning a value of one if the spectrum belongs to a particular group (according to file name), or zero if it does not belong to that group. Calibration is then developed by regressing optical data on the ‘‘reference values’’ (zero or one) of the dummy variables, and cross validation is used to test the accuracy of the calibration at each step, as a new PLS factor is added to the equation, until a minimum standard error of cross validation value is attained (Murray et al., 2001). The same procedure was used to model a discriminant function for grading categories, with six files in the calibration (V, A, C, U, N and O), and for breeds, with two files (Friesian and Hereford). 2.4. Reference analysis for chemical composition A sub-set of 72 samples combining 6 grading categories, 3 muscles and 4 replications (2 Herefords and 2 Friesians, per muscle type and grade class) was utilized for reference analysis in order to develop calibrations to predict chemical composition. After taking spectra, each sample was split into two sub-samples. One was refrozen and sent to an external laboratory for determining collagen (method of hydroxyproline) and the other was freeze-dried for at least 68 h at 10 C and subsequently analyzed in duplicate in our laboratory, following methods of AOAC International (1996) for crude protein (Kjeldahl N6.25), ether extract, and total ash (furnace at 550 C). Dry matter was determined by freeze drying and subsequent oven drying at 105 C for 12 h. Reference composition was expressed on a fresh basis for developing NIRS calibrations. 2.5. Statistical analysis of reference data In order to evaluate differences in chemical composition among muscles and classes, the data was subjected
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to a three way analysis of the variance (breeds, muscles and classes), with 72 observations in the data set. When differences (F test) were detected (P < 0.05) means were compared by the least significant difference with a proprietary software. 2.6. Calibrations for chemical composition Calibrations were developed for each constituent (software NIRS 3) testing several mathematical treatments (Osborne, Fearn, & Hindle, 1993) by combining different derivative orders (0–4), subtraction gaps (0–20 data points) and segments for a first smooth (1–20 data points). A second smooth option was always set at one data point. Besides, the combined scatter correction programs standard normal variate (SNV) and ‘‘detrend’’ were or were not applied. Of all the possible combinations, at least 20 treatments were applied, for each chemical variable. Regression equations were computed by modified partial least squares (MPLS). Other options set for the calibration process were as follows. Critical value for ‘‘T’’ outliers (NIRS-lab residuals) was set at 2.5 and for ‘‘H’’ outliers (spectral distance from the population mean) the cut-off value was set at 10, in order to force all samples into calibration. Maximum number of terms for the equations was set to 10, and 5 groups were defined for a full cross validation process. In such a way, an equation is generated with 80% (4/5) of the samples and the 20% kept out is predicted as an external validation. The process is cyclically repeated until all samples (the five groups) have been predicted. The first criterion of selection of the best equations was based on a minimum for the standard error of cross validation (SECV) and the second on a maximum value of the coefficient of determination of cross validation (R2CV).
3. Results and discussion 3.1. Composition of samples Compositional values of beef samples appear in Table 1, expressed on a fresh basis, that is, in the same form as samples were scanned. Table 1 Basic statistics for chemical composition of samples included in the calibration set (% on a fresh basis) Component
Mean
Standard deviation
Range
Dry matter Crude protein Ether extract Total ash Collagen
24.12 20.18 2.11 1.42 1.05
1.38 1.26 1.09 0.05 0.39
21.45–26.75 18.25–22.56 0.47–6.10 0.93–1.15 0.31–1.91
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A wide and even distribution in composition, along with precise reference analysis techniques are recognized as important characteristics of the calibration set of samples, in order to obtain a successful equation (Murray, 1986). This is partially the case in this work, as the range in composition for all fractions appears to be rather narrow, with a span of 5.3, 4.3, 5.6, 0.2 and 1.6 percentage units, for dry matter, protein, fat, ashes and collagen, respectively, in relation to other reports, particularly with respect to dry matter and fat content (Isaksson et al., 1996; Togersen et al., 1999). This could result in a lack of sufficient variability as to obtain sound regressions from spectra and analytical data. The selection criteria of carcasses that included a moderate fat cover, along with the fact that dissected muscles were sampled, could partially explain the narrow ranges in composition obtained in the present work. In the study of Lanza (1983) beef samples in the calibration set showed a moderately higher content of dry matter, similar content of protein and a higher content of fat. At the same time, in the case of fat, her data reveal a wider range of composition, which could suit better a calibration procedure. Mitsumoto et al. (1991) also working with dissected beef muscles, obtained a substantial wider composition, particularly on fat (2.6– 22.9% on a fresh basis), which could probably be explained by the fact that they sampled heavy weight (presumably much fatter) steers of up to 680 kg body weight. In spite of the lack of wide variability in the data as pointed before, the ANOVA detected differences (P < 0.05) between breeds, as well as among muscles and grading classes (Table 2). Average composition (muscles and classes) of samples from the Hereford breed showed a higher content of fat and lower content of protein than those from Friesians. This is to be expected as the former is an earlier maturing breed. Muscles showed differences in composition, with Supraspinosus having a lower content of dry matter than Longissimus and Semitendinosus. Protein content was higher for Longissimus and lower for Supraspinosus, with Semitendinosus at an intermediate level. A higher fat content could be expected for Supraspinosus, as it showed a lower dry matter and protein content than Longissimus and Semitendinosus. Although a slight trend to a higher fat content could be seen, the effect was not significant (P=0.10). Total ash presented slight but significant (P < 0.05) differences, with Semitendinosus showing a higher content than Supraspinosus and lower than Longissimus. The latter also had less (P < 0.05) collagen content than the rest, indicating a lower content of connective tissue. Samples from different grading classes, on average, were very similar in composition, with the exception of calves (class O), which, as to be expected, presented a
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Table 2 Average content of dry matter, crude protein, ether extract and collagen, for meat samples according to breed, muscle and grading class (% on a fresh basis)a Chemical fraction Dry matter
Crude protein
Ether extract
Total ash
Collagen
Breed Friesian Hereford LSDb
24.01 a 24.39 a 0.5161
20.40 a 19.97 b 0.3352
1.79 b 2.44 a 0.4528
1.03 a 1.05 a 0.0187
1.02 a 1.08 a 0.1419
Muscles Longissimus Semitendinosus Supraspinosus LSD
24.93 a 24.51 a 23.17 b 0.6321
21.07 a 20.62 b 18.87 c 0.4106
1.93 a 1.96 a 2.47 a 0.5545
1.05 b 1.08 a 0.99 c 0.0229
0.72 b 1.18 a 1.25 a 0.1738
Classes V A C U N O LSD
24.53 a 24.42 a 24.65 a 24.35 a 24.21 a 23.06 b 0.8939
20.49 a 20.40 a 20.52 a 20.07 a 19.33 b 20.31 a 0.5806
2.51 a 2.16 a 2.41 a 2.15 a 2.47 a 1.01 b 0.7843
1.03 a 1.05 a 1.04 a 1.06 a 1.03 a 1.05 a 0.0324
1.09 a 1.09 a 1.18 a 0.93 a 1.02 a 1.00 a 0.2458
a b
Means with different letters (a, b) for a given chemical fraction (columns) among breeds, muscles or classes, are significantly different (P >0.05). Lsd.=Least significant difference.
slightly but significant higher moisture content, as well as a distinct lower content of fat, in comparison to other classes. The other exception was related to a lower content of protein (P < 0.05) for the oldest animals (N) in comparison to the rest of the classes. Collagen content was similar among classes, suggesting that the amount of this type of connective tissue is not dependent of the age of the animals. What is expected instead, is that as animals grow older, there is an increased cross-linking and insolubility of the collagen fibers, which, other factors being equal, tend to confer an increased toughness to their meat (Lawrence & Fowler, 1997). 3.2. NIR spectra of samples Average absorbance of near infrared radiation for meat samples are presented in Fig. 1, as log (1/R). Strong absorption bands can be observed at a number of wavelengths, as described by Osborne et al. (1993) especially those associated to water (1440 and 1960 nm). Strong water absorption is expected as water is the main component of fresh samples (ranging from 73 to 78%). Several bands are also apparent for protein (1510, 1980, 2050 and 2180 nm) and fat (1760 and 2310 nm). Apart from the bands described above, other can be seen below the 1100 nm, with differences attributed to water (964 nm), fat (928 nm) and protein (908 nm). Within the visible range (400–700 nm), different absorption bands are apparent among muscles and grading classes, which can be explained by the Soret band (in the blue region of the spectrum due to a heme protein), oxymyoglobin
and myoglobin, the muscle pigments, which usually have strong absorption in this area (Cozzolino & Murray, 2002; Mitsumoto et al., 1991). Although lines show a similar trend, differences may be observed for breeds (Fig. 1a), muscles (Fig. 1b) and for classes (Fig. 1c), over the visible-NIR wavelength range. This is to be expected, as in the case of muscles for instance, Supraspinosus showed a lower dry matter, protein and ash content and twice the fat content (P < 0.1), compared to other muscles. Longissimus had a lower collagen, but higher protein content, in comparison to other muscles (Table 2). To reduce effects of factors that cause baseline shifts (particle size, water content, etc.) and to allow resolution of overlapping peaks, emphasizing useful spectral information, a math treatment of 2-5-5 was applied to the spectral data. This means that a first and then a second subtraction was performed on absorbance data at a gap of 5 data points across the spectrum, after smoothing segments of 5 data points. This modification is presented in Fig. 2, covering the NIR range, from 1100 to ca. 2483 nm. Baseline shift has been almost eliminated, some overlapping has been resolved and differences in absorption among breeds (Fig. 1a), muscles (Fig. 1b) or classes (Fig. 1c) are restricted to some significant wavelengths, which could be useful to perform calibrations. 3.3. Discriminant analysis Different calibrations were developed applying several math treatments, to obtain discriminant equations, in
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Fig. 1. Average NIR spectra, as log (1/R), for meat samples from (a) two breeds, (b) three muscles and (c) six grading classes.
order to recognize samples belonging to a particular breed, muscle or grading class (Table 3). Results for better equations for breed identification show that 78% of the samples could be correctly
assigned to the breed they belong to. This was performed with the same model for both breeds, including 15 PLS terms and without any math treatment, other than a first smooth for segments of ten data points.
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Fig. 2. Average NIR spectra as second derivative, for meat samples from (a) two breeds, (b) three muscles and (c) six grading classes.
The models developed for muscle recognition were able to identify almost all samples of Longissimus (44 of 45) and Supraspinosus (43 of 44), and to a lesser extent the samples of Semitendinosus (34 of 38). Different
models were selected for each muscle. The best calibration for Longissimus was obtained on the raw data, as log (1/R), with a smooth for a segment of 10 data points and 15 PLS terms in the equation. The only misclassified
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D. Alomar et al. / Meat Science 63 (2003) 441–450 Table 3 Classification results from selected NIRS discriminant equations for breed, muscle and grading class Data pre-treatment
No. PLS factors
Belongs to
Correctly classified
Trait None None
15 15
None First derivative Second derivative
15 13 10
First derivative First derivative First derivative First derivative First derivative First derivative
9 13 12 13 9 9
Breed Friesian Hereford Muscle Longissimus Semitendinosus Supraspinosus Grading class V A C U N O
sample was assigned to Semitendinosus muscle. This same equation was able to discriminate a high proportion of the samples belonging to the other muscles: 42 out of 44 samples were correctly assigned to Supraspinosus (the remaining 2 misclassified as Semitendinosus) and 33 of 38 samples accurately assigned to Semitendinosus (the remaining 5 assigned to Longissimus). For Supraspinosus the best model included a second derivative in the math treatment, over a gap of 20 data points and a smooth of a segment of 20 data points (220-20), with 10 PLS terms. The best calibration for Semitendinosus included 13 PLS terms and was accomplished with a first derivative, subtracting the spectral data over a 10 data point gap and with a first smooth over a segment of 10 data points. It misclassified 3 samples as Longissimus and 1 as Supraspinosus. As can be seen in Table 3, it was not possible to obtain proper discrimination models for most grading classes. Only 11 out of 25 samples of the V category, representing meat of the best quality, were correctly classified, resulting in the lowest accuracy among classes. Only meat from calves (class O) could be predicted with good accuracy, as 94% (17 from 18) of the samples were correctly identified. The only misclassified sample by this equation, was graded as V. This model included a first derivative and 9 PLS terms in the equation. The model for class N was ranked next, with 14 correct out of 21 samples (66.7%). The ability of a NIRS model to discriminate is based on the vibrational responses of chemical bonds to NIR radiation and it is probable that the higher the variability in these chemical entities, which respond to this range of the electromagnetic spectrum, the better the accuracy of the model can be. Spectra of different grading classes show variability (Fig. 1c) even after a second derivative math treatment (Fig. 2c), although
n
n
%
61 66
48 52
78.7 78.8
45 38 44
44 34 43
97.8 89.5 97.7
25 17 21 21 21 18
11 7 8 10 14 17
28.9 41.2 38.1 47.6 66.7 94.4
less variation is apparent in the latter. It is probable though, that a high variability within each class prevented the accomplishment of satisfactory equations to discriminate among classes. This high within class variability probably results from the mixing of different muscles (and different breeds) in each class, muscles which in turn differed in several chemical fractions. This could explain why muscles were discriminated much better than classes. Chemical composition among grading classes, on the other hand, was very similar on average, with the exception of samples from class O which, accordingly, was the only one to be accurately predicted. It can be presumed that models for grading categories could be improved if they were adjusted independently for each muscle group. This only could be attained including a much higher number of samples in the data set, which was not possible in the present experiment. 3.4. Prediction of composition Results of calibrations are shown in Table 4, where the indexes of certainty (R2CV) and uncertainty (SECV) for the cross-validation process were applied as indicators of the relative merit of the best equations generated. The best ranked equation for each fraction is described in terms of the statistics mentioned above, along with the respective math treatment and the mean and standard deviation of the reference values of the samples retained in the calibration. According to the results, the prediction of the dry matter content could be uncertain, as the value for R2CV is under 80% and, although the SECV value conforms to a rather small fraction of the average dry matter value, it is not small enough when it is related to the spread of the reference values, as to give reliable predictions. Kennedy, Shelford, and Williams (1996) argue
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Table 4 Mathematical treatment (derivative–subtraction gap–smooth segment) and statistics results for the best NIRS calibrations developed for predicting beef composition Trait
No. PLS terms
R2CVa
SECVb
Math treatment
Mean
Standard deviation
Dry matter Crude protein Ether extract Total ashes Collagen
6 3 3 4 1
0.77 0.82 0.82 0.66 0.18
0.58 0.48 0.44 0.03 0.30
1-10-10 SNV+Det 2-20-20 2-10-10 0-0-10 SNV+Det 2-20-20
24.22 20.27 2.07 1.05 1.00
1.18 1.12 1.04 0.05 0.33
a b
Coefficient of determination of cross validation. Standard error of cross validation.
Fig. 3. Reference and NIRS predicted values (%) for dry matter (DM), crude protein (CP) and fat as ether extract (EE). Diagonal represents equal response line.
that an equation is considered to be acceptable if the ratio of the standard deviation of reference data to the standard error of prediction is higher than three. Although an external validation was not performed here, the same criterion has been assumed for the crossvalidation process (Cozzolino & Murray, 2002). The best calibrations for protein and fat were obtained with a second order derivative (2-20-20 and 210-10, respectively) and without any scatter correction of the spectral data. The R2CV (0.82 for protein and 0.84 for fat) reveals a strong relationship between spectra and composition, which represents good quantitative information (Shenk & Westerhaus, 1996), but the respective SECV values (0.48 for protein and 0.44 for fat) lead to conclude that these equations can be helpful in estimating a ‘‘near’’ content, but are not reliable enough to achieve a good accuracy in predicting these fractions. The lack of a strong predicting ability for protein in meat is not new, and several authors have reported similar results (Cozzolino & Murray, 2002; Kruggel et al., 1981; Lanza, 1983; Mitsumoto et al., 1991). The total ash fraction was poorly predicted, as a R2CV value of 0.66 could only be useful for discriminating (qualitative analysis) samples of high, medium or low ash values. This is not surprising, as near infrared radia-
tion does not interact with pure minerals or inorganic compounds as ionic forms and salts, unless minerals are related to the organic fraction, either through associations with organic acids, chelates, or forming salts which, although non absorbing in the near infrared region, can affect hydrogen bonding in moist samples (Shenk & Westerhaus, 1995). No useful regression could be obtained for collagen content, with a R2CV of 0.18 for the best calibration obtained and a SECV close to the spread in composition. This was also the case in the work of Calvo, Lo´pez, Sa´nchez, Dios, and Sa´nchez (1997), who tried to predict composition of Gallego lambs. Mitsumoto et al. (1991) on the other hand, found a correlation between hydroxyproline (used as an estimator of collagen content) and optical density, at around 955 and 1080 nm. If the poor results obtained in the present work were caused by sampling or analytical errors, or by the lack of sufficient variability of the collagen content of samples, could not be established and deserves further work. Calibrations with a math treatment with a high (third or fourth) derivative order improved models for some fractions (especially protein). These treatments are not common in NIRS literature, as most reports do not normally exceed a second order derivative. An exception is the work by Fumie´re, Sinnaeve and Dardanne
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(2000) on discriminant models for chicken meat, but the analytical performance of the higher order models is not presented by the authors. According to Shenk and Westerhaus (1995), the best math treatment can only be attained by trial and error, without excluding a third or fourth order derivative. On the other hand, Osborne et al. (1993) point out that these higher subtraction orders are rarely used, as they tend to deteriorate the signal to noise ratio by magnifying the noise, and also tend to increase the complexity of the spectrum. In the event that a higher derivative could result in a lack of long-term stability for the equations, a higher samplesensitivity, or susceptibility to noise, a first or second derivative could still be used, although with a degree of uncertainty, especially for protein and ether extract in the present work. Subsequent tests in order to inspect the stability of selected equations of higher derivative order were not performed, as meat samples could not be kept long enough. Composition predicted by NIRS and values obtained by reference methods are plotted in Fig. 3. For the reasons mentioned above, only data from equations up to a second derivative were used. Even though points are rather ‘‘scattered’’, they follow a pattern around the equal response line, suggesting that the spectra change according to chemical composition and that with an appropriate data management the chemical features can be elucidated. It is probable that more striking results, that is, closer correlations and lower standard errors, could be expected with a larger set of calibration samples and with a wider distribution in composition, particularly for dry matter, in both extremes (below 23 and above 26%) of the moisture range and for ether extract, specially in the highest range (above 4%) of fat content. In spite of the above, the results reported here lead to conclude that NIRS can be used as a helpful tool for identifying breed and muscle type in beef meat, on an objective, composition-related basis and to achieve fast predictions of meat composition with reasonable accuracy, particularly for protein and fat content. Further work, including larger data sets and working with separate muscles, should be carried out in order to develop models capable to discriminate meat according to the Chilean classification system.
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