Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes

Animal Feed Science and Technology 111 (2004) 161–173

Exploring the use of near infrared reflectance spectroscopy (NIRS) to predict trace minerals in legumes D. Cozzolino a,∗ , A. Moron b a

Instituto Nacional de Investigacion Agropecuaria, Animal Nutrition, INIA La Estanzuela, Colonia, Uruguay b Soil Department, Instituto Nacional de Investigacion Agropecuaria, INIA La Estanzuela, Ruta 50, km 12, CC 39173, Colonia, Uruguay Received 28 January 2003; received in revised form 23 July 2003; accepted 5 August 2003

Abstract The use of near infrared reflectance spectroscopy (NIRS) was explored to predict trace mineral concentrations in two legumes. Samples (332), composite of white clover (n = 97) and lucerne (n = 235), from different locations in Uruguay representing a wide range of soil types, were analysed for sodium (Na), sulphur (S), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), and boron (B). The samples were scanned in reflectance in a monochromator instrument (400–2500 nm). Calibration models (n = 262) were developed using modified partial least squares regression (MPLS) based on cross-validation and tested using a validation set (n = 70). Two mathematical treatments of the spectra were compared (first and second derivative). The highest coefficients of determination in calibration (R2CAL ) and the lowest standard errors of cross-validation (SECV) were obtained using second derivative. The R2CAL and SECV were 0.83 (SECV: 0.8) for Na and 0.86 (SECV: 2.5) for S in g kg−1 DM; 0.80 (SECV: 4.4), 0.80 (SECV: 10.6), 0.78 (SECV: 22.9), 0.76 (SECV: 0.83) and 0.57 (SECV 25.7) for B, Zn, Mn, Cu and Fe in mg kg−1 DM on a dry weight, respectively. Sulphur (SEP: 5.5), sodium (SEP: 1.2) and boron (SEP 4.2) were well predicted by NIRS on a validation set. © 2003 Elsevier B.V. All rights reserved. Keywords: Legumes; Forage quality; Trace minerals; Partial least squares; NIRS

Abbreviations: NIRS, near infrared reflectance spectroscopy; MPLS, modified partial least square regression; SEC, standard error of calibration; SECV, standard error of cross-validation; R2CAL , coefficient of determination in calibration; SEP, standard error of prediction; Na, sodium; Cu, copper; Fe, iron; Mn, manganese; Zn, zinc; S, sulphur; B, boron; SNVD, standard normal variate and detrend; S.D., standard deviation; D1, first derivative; D2, second derivative; CV, coefficient of variation ∗ Corresponding author. Present address: The Australian Wine Research Institute, Waite Road, P.O. Box 197, Glen Osmond, SA 5064, Australia. Fax: +61-88303-6601. E-mail addresses: [email protected] (D. Cozzolino), [email protected] (A. Moron). 0377-8401/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.anifeedsci.2003.08.001

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1. Introduction The primary reason for the production of forage is to provide the feed for livestock. Legume forage crops have become increasingly important in intensive animal systems in Uruguay during recent years. Particularly lucerne (Medicago sativa L.) gained in importance due to its high productivity and quality combined with high DM yield during the spring–summer season. In the past 100 years, it became apparent that nutrient content as well as yield measurements should be considered in deriving proper animal feeding systems (Deaville and Flinn, 2000). Laboratory methods were developed and refined to provide nutrient information to the industry, farmers and researchers. However, classic evaluation of these products are expensive and time-consuming (Osborne et al., 1993; Shenk and Westerhaus, 1994; Deaville and Flinn, 2000). Near infrared reflectance spectroscopy (NIRS) has been introduced as a rapid, inexpensive, and accurate method for the analysis of grain, oilseeds, and forages (Norris et al., 1976; Murray, 1993; Batten, 1998). The technique is based on the correlation between chemical properties, as determined by defined reference methods, and absorption of light at different wavelengths in the near infrared region, measured by reflectance. The near infrared region contains information concerning relative proportions of C–H, N–H, and O–H bonds, which are the primary constituents of the organic molecules in forages (Osborne et al., 1993; Coleman and Murray, 1993). NIRS relies on calibrations, which utilise absorbances at many wavelengths, to predict composition of a feed sample (Murray, 1986a; Batten, 1998). NIRS was first employed to analyse large batches of pastures and forage samples for quality factors including crude protein, neutral detergent fibre, lignin, and in vitro organic matter digestibility (Norris et al., 1976; Shenk et al., 1979; Marten et al., 1984; Barton and Windham, 1988; Deaville and Flinn, 2000). Minerals in pastures and agricultural products probably exist in both inorganic and organic complexes. The concentration of inorganic components in forage crops varies according to crop maturity, plant parts, temperature, amount of fertilisers applied to the crop, soil fertility and soil physical characteristics (MacPherson, 2000). Reference methods for mineral analysis include inductively coupled argon plasma (ICP), atomic absorption spectroscopy (AAS), and X-ray fluorescence spectroscopy (XRF) (Shenk et al., 1992). Because NIRS measures absorption by molecular bonds, its use for pure minerals may seems to have no sense. However, prediction of some minerals in forages by NIRS may be possible through their association with the organic matrix (Givens and Deaville, 1999). Only a few reports were found related to the use of NIRS for macro and trace minerals in both grasses and hay samples (Clark et al., 1987, 1989; Saiga et al., 1989; Smith et al., 1991), in natural grasses (Vazquez de Aldana et al., 1995), and in botanical fractions of semi-arid grasslands (Ruano-Ramos et al., 1999). The aim of this study was to explore the potential and accuracy of NIRS for predicting sodium (Na), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), sulphur (S) and boron (B) in legume forage samples. 2. Materials and methods 2.1. Legume samples During 1997 and 1998, samples of lucerne (n = 235) (M. sativa L.) and white clover (n = 97) (Trifolium repens L.) were collected from several commercial farms in Uruguay

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(South America) which varied in location, soil characteristics (texture, organic matter, N content, pH) and farm management. The samples came from commercial farms located in the south region (Departments of Florida, San José and Canelones) as well as the north region (Departments of R´ıo Negro and Paysandú) (Uruguay regional division). Legume samples solely originated from pure species, single crop or from mixtures with winter crops (wheat or barley) were collected. Samples were harvested by hand at the end of winter (1–2 years after sowing) (July–August in the southern hemisphere), prior to flowering, to avoid increasing variations on trace elements due to different growth stages. Samples were cut at an average height of 27 cm (coefficient of variation (CV) 33), leaving approximately 15 cm (including stems and leaves). A minimum of six individual subsamples was bulked to provide 332 composite samples. 2.2. Reference analysis Samples were dried in a forced-air oven at 60 ◦ C for 48 h. The samples were ground using a Wiley mill (1 mm sieve) (Arthur H. Thomas, Philadelphia, USA). Sulphur, copper, manganese, iron, sodium, zinc and boron were determined by atomic absorption spectrophotometry (Cornforth, 1984; Mills and Jones, 1996; Pinkerton et al., 1997). 2.3. NIRS analysis and calibration development The samples were scanned as dry ground powder in reflectance mode (400–2500 nm) using a NIRSystemsTM 6500 monochromator (NIRSystems, Silver Spring, MD, USA) in a small circular cup (50 mm diameter) (Part number IH-0325, NIRSystems, USA). Samples were scanned once and were not rotated when spectra collection was made. Reflectance data were stored as the logarithm of reciprocal of reflectance (1/R) at every 2 nm interval to give a total of 1050 data points. Two pairs of lead sulphide detectors collected the reflectance spectra and the reflectance energy readings were referenced to corresponding readings from a ceramic disk. The spectrum of each sample was the average of 32 successive scans (16–32–16). Infrasoft International software 3.01 version (Infrasoft International, (ISI), Port Matilda, PA, USA) was used for near infrared spectral data collection, spectral processing and calibration development. Calibration equations were developed on 262 samples randomly selected from the population set, using modified partial least squares regression (MPLS) (Shenk and Westerhaus, 1993) with cross-validation (NIRS 2, 1995). Spectra were corrected for scattering by using standard normal variate (SNV) and detrend (Barnes et al., 1989). Modified partial least squares was used because it can deal with non-linearity problems and produce accurate methods for the analysis of agricultural products (Dardenne et al., 2000). Additionally, two derivative treatments of the spectra were compared, (1, 4, 4, 1) and (2, 5, 5, 2), where the first number indicates the order of derivative (one is first derivative of log 1/R), the second number is the gap in datapoints over which the derivative is calculated, and the third and fourth number refer to the number of data points used in the first and second smoothing, respectively. Calibration statistics included the standard error of calibration (SEC), the coefficient of determination in calibration (R2CAL ), the standard error of cross-validation (SECV), and the coefficient of determination in cross-validation (R2VAL ) (Shenk and Westerhaus, 1993; NIRS 2, 1995). Optimum calibrations were selected based

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on the highest R2CAL and lowest SECV. Besides the internal validation, the calibrations were tested on the remaining third of the sample set (n = 70). Thereby, the coefficient of simple correlation (RSQ), the standard error of prediction, the slope and bias were calculated. The outlier elimination pass was set to allow the software to remove outliers twice before completing the final calibration (NIRS 2, 1995). Principal components (PC) were derived from the spectral data to calculate distances and to enable distribution and structure of the samples to be viewed graphically. The CENTER program ranks spectra in a file according to their standardised Mahalanobis distance (H statistic) from the average spectrum of the file using PC scores. The Mahalanobis distance of each spectrum with respect to the average spectrum was calculated. Samples with an H statistics value greater than 3 units from the mean spectrum were defined as outliers and removed when calibrations were developed. The SECV/S.D. ratio was also calculated to evaluate the performance of the calibrations (Murray, 1986a, 1993).

3. Results 3.1. Reference values The mean, range and standard deviation of the trace minerals measured by the reference method are shown in Table 1. Trace minerals showed a wide range in composition due to different species, soil characteristics, farm management and regions. The means are considered to lie within the normal ranges and representing the type of values that can be found for legume species in Uruguay (Cozzolino et al., 1994). Low Pearson correlation (P < 0.05) were found among the reference data for trace minerals (r less than 0.20), demonstrating weak relationships between these elements. 3.2. Spectra interpretation The mean near infrared spectrum of the legume samples (Fig. 1) showed absorption bands at 1194 nm related with C–H stretch second overtone, at 1498 nm related with O–H stretch second overtone, at 1730 nm related with C–H stretch first overtone, at 1940 nm Table 1 Mean, standard deviation (S.D.) and range for trace minerals in legume plant samples (n = 332) Minerals

Na (g kg−1 ) S (g kg−1 ) Cu (mg kg−1 ) Fe (mg kg−1 ) Mn (mg kg−1 ) Zn (mg kg−1 ) B (mg kg−1 )

Calibration (n = 262)

Validation (n = 70)

Range

Mean

S.D.

Range

Mean

S.D.

0.2–6.8 20–72 5–31 66–861 34–282 19–417 21–60

2.3 30 8 130 90 53 35

1.7 9 2 89 48 19 9

0.2–7.0 18.3–68.0 5–31 67–510 32–234 19–417 21–66

2.3 32.4 8 120 87 56 33

1.8 8.8 2 91 42 26 8

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Fig. 1. Near infrared mean spectrum (whole line) and standard deviation (dotted line) of legume samples.

with O–H stretch first overtone (water), and at 2164, 2310 and 2348 nm related with C–H combination tones or amide C–O stretch combination tones, respectively (Murray, 1986b). High standard deviations of the absorbances were found around the water region (at 1400 and 1900 nm) and at 2300 nm C–H stretch first overtone and at C–H combinations tones, respectively (see Fig. 1). Fig. 2(a) and (b) show the wavelength correlation between elements after SNVD and second derivative treatment for Na, B, Mn and Fe. High correlations were found between wavelength and trace minerals in legume samples around 1100–1300, 1400 and 1900–2200 nm. These regions had considerable influence in the spectra due to the strong relationship between trace minerals and other constituents, principally with O–H tones (water) and with C–H combination tones (organic functional groups) (Murray, 1986b; Garnsworthy et al., 2000). In the visible region (400–700 nm), even higher correlations were observed due the relationship between these minerals either with plant pigments or organic substances (Clark et al., 1989; Vazquez de Aldana et al., 1995). Other authors analysing minerals in forages reported similar absorption regions, although the wavelengths are not expected to be identical. Because, trace elements are found in different complexes and the complexes appear to be different both within and among forages, and this will lead to differences in wavelengths selected (Clark et al., 1989; Vazquez de Aldana et al., 1995; Ruano-Ramos et al., 1999). 3.3. Calibration and validation Table 2 shows the calibration and cross-validation statistics for the prediction of trace elements using spectra after SNVD and either first or second derivative treatment. The

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highest R2CAL (≥0.80) were obtained for Na, S, B and Zn applying the second derivative. Intermediate correlations (0.70 < R2CAL < 0.80) were obtained for Mn and Cu, where the lowest correlation (R2CAL < 0.70) were found for Fe. It is well known that the SECV/S.D. ratio for NIRS calibration statistics for the parameters evaluated demonstrate how well the calibration models performed for the chemical data. We attempted to classify the suitability of reference methods for NIRS calibration, according to the relationship between the error

Fig. 2. (a) Wavelength correlation between trace elements in legume samples using SNVD and second derivative as treatment. (b) wavelength correlation between trace elements in legume samples using SNVD and second derivative as treatment.

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Fig. 2. (Continued ).

in analysis and the spread in composition (Murray, 1986a, 1993). If a product shows a narrow range in composition, or if the error in estimation is large compared with the spread (as S.D.) in composition, then regression finds increasing difficulty in finding stable NIR calibrations. Where the error exceeds one-third of the SD of the population, regression can be misleading (Table 2) (Murray, 1986a, 1993). The results showed stable calibration for S and intermediate for B, Cu and Na. Table 3 presents the statistics (bias, slope, SEP, and RSQ) for the prediction of trace elements concentrations in legumes. None of the trace elements were well predicted by the NIRS calibration models developed. Figs. 3–5 show the relationship between NIRS predicted data and values measured by the reference method for Na, B and S, respectively. The more accurate the predictive equation, the more closely

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Table 2 Calibration and cross-validation statistics for legume samples using SNVD and first and second derivatives Minerals

Na

R2CAL b

SECc

R2VAL d

SECVe

SECV/S.D.f

D1g Na (g kg−1 ) Cu (mg kg−1 ) Fe (mg kg−1 ) Mn (mg kg−1 ) Zn (mg kg−1 ) B (mg kg−1 ) S (g kg−1 )

261 254 249 254 249 257 245

0.81 0.61 0.51 0.57 0.76 0.80 0.84

0.7 0.85 41.8 16.3 8.9 3.8 2.3

0.80 0.56 0.44 0.40 0.65 0.77 0.80

0.8 0.91 43.3 22.2 11.8 4.2 2.6

0.47 0.46 0.48 0.42 0.62 0.46 0.28

D2h Na (g kg−1 ) Cu (mg kg−1 ) Fe (mg kg−1 ) Mn (mg kg−1 ) Zn (mg kg−1 ) B (mg kg−1 ) S (g kg−1 )

255 256 249 255 250 259 245

0.83 0.76 0.57 0.78 0.80 0.80 0.86

0.7 0.66 40.6 16.9 7.8 4.1 2.2

0.74 0.62 0.44 0.64 0.66 0.77 0.82

0.8 0.83 45.7 20.9 10.6 4.4 2.5

0.47 0.41 0.51 0.47 0.55 0.48 0.27

a

N: number of samples used to perform the calibration models.

b R2 CAL : coefficient of determination in calibration. c SEC: standard error in calibration. d R2 VAL : coefficient of determination in cross-validation. e SECV: standard error of cross-validation. f SECV/S.D.: standard error of cross-validation to standard g D1: first derivative. h

deviation ratio.

D2: second derivative.

Table 3 Prediction statistics for microelements in legume samples using first and second derivative (validation set: 70 samples) RSQa

SEPb

Bias

Slope

Na (g kg−1 ) Cu (mg kg−1 ) Fe (mg kg−1 ) Mn (mg kg−1 ) Zn (mg kg−1 ) B (mg kg−1 ) S (g kg−1 )

0.63 0.07 0.30 0.43 0.34 0.72 0.65

1.2 2.3 53.8 32.8 46.1 4.4 5.6

0.011 0.15 9.7 0.98 7.5 0.33 0.14

0.86 0.68 1.26 0.84 1.4 1.01 1.22

D2d Na (g kg−1 ) Cu (mg kg−1 ) Fe (mg kg−1 ) Mn (mg kg−1 ) Zn (mg kg−1 ) B (mg kg−1 ) S (g kg−1 )

0.61 0.07 0.30 0.41 0.31 0.74 0.70

1.2 2.3 55.5 33.7 46.6 4.22 5.5

0.020 0.05 9.64 1.31 7.4 0.49 0.17

0.87 0.61 1.063 0.78 1.28 0.955 1.22

Minerals D1c

a

RSQ: coefficient of simple correlation. SEP standard error of prediction. c D1: first derivative. d D2: second derivative. b

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8

7

Na predicted by NIRS g/kg

6

5

4

3

2

1

0 0

1

2

3

4

5

6

7

8

Na determined by reference g/kg Fig. 3. NIRS predicted data vs. chemical reference data for Na (n = 70).

all points cluster near the theoretical 1:1 (solid line) correspondence. In the case of B, two clusters were observed, corresponding with the content of this element in both species.

4. Discussion The SEC, SECV and R2CAL values indicate how well the equations will perform within the same population. However, with trace minerals, the SEC and especially the R2CAL are not good indicators, because the NIR not directly measuring the element (Clark et al., 1989; Malley et al., 1999; Chang et al., 2001). The R2 values for mineral determinations are governed more by the amount or variability (range in concentration) present than by direct relationship between concentrations change and absorption in the NIR region (Clark et al., 1989). The SEC and SECV obtained in this work (Table 2) for the different trace minerals agreed with those reported by other authors (Clark et al., 1989, Vazquez de Aldana et al., 1995). Vazquez de Aldana et al. (1995) reported similar results for Mn (R2 : 0.74 and SEC: 50), Cu (R2 : 0.82 and SEC:

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55

B predicted by NIRS mg/kg

50

45

40

35

30

25

20 20

25

30

35

40

45

50

55

60

B by reference method m/kg Fig. 4. NIRS predicted data vs. chemical reference data for B (n = 70).

0.84), Zn (R2 : 0.72 and SEC: 3.8) and better for Fe (R2 : 0.74 and SEC: 15) as mg kg−1 DM. In all the trace elements analysed, the SEC values are lower than the S.D., indicating that NIRS could be used to determine concentration changes for Na, S, B, Zn, Cu and Mn in the legume samples. When samples not included in the calibration (validation set) were predicted using the NIRS models the best prediction results were obtained for B, Na and S. Poor calibrations for Fe were attributed to sample soil contamination and agreed with those reported by other authors (Vazquez de Aldana et al., 1995). A moderate number of outlier samples were observed (less than 10%) due to contamination with either other species (wheat or barley), soil or weeds when the NIRS models were developed. From the statistical values, it appears that NIRS is somewhat sensitive to the presence of some trace elements, depending of the forage type (e.g. grasses versus legumes), or species (e.g. alfalfa versus white clover) (Ruano-Ramos et al., 1995). NIRS measures bonds within organic compounds, which are negatively related to inorganic materials. If the mineral matter is bound within organic compounds the distortion of the spectrum may be detectable at certain wavelengths, suggesting that NIRS could predict inorganic materials using their relationship between organic matter (Osborne et al., 1993; Garnsworthy et al., 2000). Some minerals (e.g. Ca and P) do not have absorption in the near infrared region but could be indirectly detected through their linkage with organic complexes, chelates, and pigments such as

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80

S predicted by NIRS g/kg

60

40

20

0 0

20

40

60

80

S by reference method g/kg Fig. 5. NIRS predicted data vs. chemical reference data for S (n = 70).

chlorophyll in forages (Ruano-Ramos et al., 1995). Near infrared calibrations for trace elements in legumes suggest that absorption band for organic acids might exist in the near infrared region, although there is no direct evidence from the results obtained in this work. It is also possible that NIRS calibrations make use of naturally occurring correlation between trace minerals and other constituents, such as organic matter, lignin, protein and pigments (Shenk et al., 1992; Givens and Deaville, 1999; Garnsworthy et al., 2000). The wavelength correlations (Fig. 2(a) and (b)) for S showed specific spectral patterns at 640, 568 nm related with plant pigments; around 1900 nm related with OH overtones and at 2300 nm related with S–H overtones and agreed with those reported by other authors (Clark et al., 1989). For Na, the correlations were predominantly observed at 1400 and 1900 nm related with O–H overtones. The wavelength correlations for trace elements showed spectral patterns around the visible region, related with plant pigments (400–700 nm) and similar spectral patterns among them along the NIR region, principally associated with OH overtones (Fig. 2(a) and (b)). According to other authors (Smith et al., 1991; Vazquez de Aldana et al., 1995), standard deviations were higher because mineral elements are found in diverse forms in different plant species, where coefficients of determination were smaller probably due to the narrow concentration range of trace minerals in herbaceous samples and low concentration

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of the associated organic compounds sensed by NIRS. Thus indicated that low correlations could be also explained in relation with the detection limit of a specific trace element by the NIR method. Although trace mineral prediction by NIRS seems very limited, the speed of analysis and minimal preparation of the sample are advantageous compared with other reference methods, for example AAS.

5. Conclusions The calibration and validation statistics obtained in this work showed the potential of NIRS to predict trace minerals in legumes, particularly B, Cu, Mn and Zn as well as two macro elements Na and S. Although the accuracy was too low for routine analysis, NIRS could be used as a screening tool to diagnose fertiliser requirements in forage crops. If exact trace mineral results are required the NIRS will not the best method to use. Further work will need to be done to improve the accuracy of for the NIRS analysis including more legume species, agronomic conditions and different years.

Acknowledgements The authors thank the technicians at the Soil Department for the plant analysis. The work was supported by INIA, Uruguay. Suggestions and comments by editorial reviewers are gratefully acknowledged.

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