Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy

Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy

Industrial Crops and Products 20 (2004) 321–329 Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy A...

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Industrial Crops and Products 20 (2004) 321–329

Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy A. Fassio1 , D. Cozzolino∗,2 Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Ruta 50, km 11, Colonia, Uruguay Received 25 April 2003; accepted 14 November 2003

Abstract Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration (n = 250) where the remaining samples (n = 50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration (R2cal ) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg−1 on a dry weight basis (second derivative, 400–2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs. © 2003 Elsevier B.V. All rights reserved. Keywords: Near infrared reflectance spectroscopy; Oil; Moisture; Crude protein; Intact seeds; Sunflower; Helianthus annuus L.

1. Introduction Sunflower (Helianthus annuus L.) is one of the most widely cultivated oil crops in the world (Canibe et al., 1999; Flagella et al., 2002). Although seed oil of standard cultivated sunflower is considered to be of good

∗ Corresponding author. Present address. The Australian Wine Research Institute, Waite Road, P.O. Box 197, Urrbrae-5064, Adelaide, Australia. Fax.: +61-8-8303-6601. E-mail addresses: [email protected] (A. Fassio), [email protected] (D. Cozzolino). 1 Summer Crop Breeding Program, INIA La Estanzuela. 2 Animal Nutrition and NIRS Lab, INIA La Estanzuela.

quality for edible purposes, in recent years, the development of cultivars with high oil content and high oleic acid concentration is an important breeding objective for this crop (Flagella et al., 2002). Plant improvement for quality purposes depends on the ability to evaluate large number of individuals (Hymowitz et al., 1974; Rubel, 1994; Kohel, 1998) where the only way to deal with the increased variability is with more frequent analysis and sampling (Murray, 1996). Usually, such ability requires chemical and physical methods of analysis that are destructive, slow and expensive in chemical reagents and labour. A major concern of both oil chemists and plant breeders is that the method available for routine analysis of grains and seeds is

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time consuming (Panford and de Man, 1990; Dardenne et al., 1995; Williams, 2001). The availability of rapid, non-destructive methods to evaluate seed quality traits is one of the most important factors determining the success of plant breeding projects (Röbbelen, 1990; Velasco et al., 1999). Near infrared reflectance spectroscopy (NIRS) is a multitrait technique of large application in the analysis of quality characteristics in food and agricultural commodities (Shenk and Westerhaus, 1993; Batten, 1998). The NIR region spans the wavelength range 780–2500 nm, in which absorption bands correspond mainly to overtones and combinations of fundamental vibrations (Batten, 1998; Blanco and Villarroya, 2002). The principle of NIRS is that light of wavelength 1100–2500 nm, reflected off powdered solids, contains compositional information, which can be explained by a computer to report multiple analysis (Murray, 1993; Batten, 1998). NIRS relies on calibrations which utilise absorbances at several wavelengths, to predict the composition of a samples (Batten, 1998). The grain from cereal crops, grown in many countries around the world, is classified on the basis of protein and moisture using NIRS instruments (Robertson and Barton, 1984; Ronalds and Miskelly, 1985; Osborne et al., 1993; Dardenne et al., 1995; Williams, 2001). Reports were found in the literature that related to the determination of both oil content and fatty acid composition in sunflower seeds (Robertson and Barton, 1984; Sato et al., 1995; Pérez-Vich et al., 1998; Velasco et al., 1999). The objective of this study was to evaluate the potential of NIRS to predict oil, crude protein (CP) and moisture (M) content as non-destructive technique in pre-screening of sunflower seed samples for breeding purposes in Uruguay.

2. Materials and methods Three hundred (n = 300) sunflower (Helianthus annuus L.) samples obtained from several breeding lines (individual plants) from the Summer Crop Breeding Program at INIA La Estanzuela were used as a material to develop the NIRS calibration models for quality parameters. The samples were split into two sets, 250 samples were used in calibration, where the remaining 50 were used as validation set. All the samples were

grown at INIA La Estanzuela (Uruguay, South America) and harvested in different years (1997–2000). Prior to oil analysis samples were cleaned, oven-dried (40 ◦ C for 4 h) and ground in a Cyclotec mill 1 mm (Foss Tecator, Sweden). For oil analysis, samples were taken immediately after grinding and analysed by Soxhlet extraction using petroleum ether as solvent for 6 h (AOAC, 1990) and corrected for moisture. Moisture (M) was determinate by oven drying the sample at 105 ◦ C for 16 h to constant weight (AOAC, 1990). Nitrogen was determined using a semi-micro automated Kjeldhal method (Tecator, Sweden) and converted to crude protein (CP) using the factor 6.25 (AOAC, 1990). Oil, CP and M content were expressed as g kg−1 on dry weight basis. Chemical analyses were performed in duplicate. Intact samples (about 6–10 achenes) were placed in a small ring cup (50 mm diameter; 20 mm depth) (Part number IH–0307, NIRSystems, USA) and scanned in reflectance in the visible (Vis) and near infrared (NIR) (400–2500 nm) region on a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Reflectance data were stored as the logarithm of reciprocal of reflectance [log (1/R)] at 2 nm intervals, collecting 1050 data points. Spectra data collection, manipulation and calibrations were developed using the NIRS 2 version 3.0 (Infrasoft international, Port Matilda, USA). Calibration equations were developed using modified partial least squares (MPLS) (Shenk and Westerhaus, 1993) regression with internal cross validation (NIRS 2, 1995). No scatter correction (log 1/R) and scatter correction using standard normal variate and detrend (SNVD) (Barnes et al., 1989) were evaluated. The SNVD is designed to remove additive baseline and multiplicative signal effects resulting in a spectrum with zero mean and a variance equal to one (Barnes et al., 1989). These algorithm enhance the differences in spectra related to the chemical composition of samples by reducing differences in spectra related to physical characteristics of the sample (primarily particle size) (Barnes et al., 1989). Cross validation was used to avoid overfitting of the equations by selecting the minimum number of PLS terms in each model (Naes et al., 2002). Two mathematical treatments were applied 1,5,5 and 2,5,5. The first number indicates the order of derivative (one is first derivative of log 1/R), the second number is the gap in data points over which the

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derivative is calculated; the third number is the number of data points used in the first smoothing (Shenk and Westerhaus, 1993). Calibration statistics calculated include 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 (R2cv ) (Shenk and Westerhaus, 1993). The optimum calibrations were selected on the basis of minimising the standard error of cross validation. This error was calculated by an internal validation of 33% of samples randomly taken by the software routine, which were predicted by an equation based on a calibration with the remaining 66% of all samples. The outlier elimination pass was set to allow the computer program to remove outliers twice before completing the final calibration (NIRS 2, 1995). Two outlier detection methods provided by the ISI software were applied, namely t and H statistics (Mahalanobis distance). The t statistics identify outliers having residuals from the reference analysis of greater than 2.5 times SEC, indicating samples whose reference analysis is in doubt. These should be re-analysed by the reference method. The H statistic outliers are samples whose spectra are atypical of all the others that make up the calibration set. They may not belong in the population. The standard deviation of the constituent data/standard error of cross validation relationship (S.D./SECV) was calculated to evaluate the performance of the calibrations (Murray, 1993). Besides the internal cross validation, the calibrations were tested using the remaining samples (n = 50). The performance of the validation set was tested based on the standard error of prediction (SEP) and the coefficient of determination in prediction (R2 ).

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Fig. 1. Near infrared reflectance mean spectrum of intact sunflower seeds (whole line) and standard deviation of absorbance (S.D.a: dotted line) (wavelength in nm).

and 2278 nm related with C–H combinations bonds (Murray, 1986). The second derivative spectra have a trough corresponding to each peak in the original spectra, removing the overlapping peaks and baseline effects (Osborne et al., 1993). Fig. 2 shows the second derivative of the mean spectrum of sunflower seed samples in the NIR region. The second derivative of the NIR mean spectrum shows absorption bands at 1200 nm related with C–H second overtone, at 1442 nm with O–H stretch overtone and at 1930 nm related to O–H first overtone (mainly water), at 1580 nm related with N–H stretch first overtone, at 1720 nm related to C–H stretch overtone (oil content), at 2098 and 2272 nm related to C–H combination bonds (Murray, 1986; Osborne et al., 1993; Pérez-Vich et al., 1998).

3. Results Fig. 1 shows the NIR reflectance mean spectrum of intact sunflower seed samples. The overall spectrum of the intact sunflower samples shows strong absorption bands related with oil and water content. Absorption bands at 1460 nm are related with O–H stretch overtone bond (water), at 1720 and 1764 nm related with C–H stretch first overtone bonds (associated with both oil and fatty acids). At 1930 nm related with O–H bonds, mainly related with water. At 2100

Fig. 2. Second derivative of NIRS mean spectrum of intact sunflower seeds (upper line) and standard deviation of absorbance (S.D.a: lower line) (wavelength in nm).

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Table 1 shows the descriptive statistics for the quality parameters used for either the calibration or the validation set. Tables 2–4 showed the NIRS calibration and cross validation statistics for M, oil and CP, respectively. Calibration equations for oil, CP and M showed a close relationship between NIR and reference values. Both second derivative and the Vis-NIR region yield the best calibration and cross validation models for the quality parameters evaluated. Table 5 shows the NIRS prediction statistics for oil, CP and M on the intact sunflower samples using the best calibration model (second derivative and Vis-NIR). Figs. 3–5 plot the NIRS predicted values against the reference values for oil, CP and M content using second derivative and SNVD as scatter correction

Table 3 Descriptive statistics for oil, moisture and crude protein on intact sunflower samples in both calibration and validation (g kg−1 ) Mean

S.D.

Min.

Max.

Calibration (n = 250) M 65.1 Oil 459.1 CP 304.1

8.0 52.8 51.0

46.0 349.0 89.0

85.2 694.2 424.0

Validation (n = 50) M 66.0 Oil 457.0 CP 305.0

7.9 53.8 54.0

52.0 354.2 97.1

86.0 682.2 407.2

M: moisture, CP: crude protein, S.D.: standard deviation, Min.: minimum, Max.: maximum.

Table 1 Calibration and cross validation statistics for moisture on intact sunflower samples (g kg−1 ) Wavelength segment (nm)

n

S.D.

R2cal

SEC

R2cv

SECV

S.D./SECV

1,5,5 SNV 1,5,5 SNV 1,5,5 SNV

400–2500 400–750 1100–2500

146 158 154

7.5 7.7 7.7

0.90 0.66 0.88

2.4 3.1 2.8

0.75 0.65 0.76

3.0 4.0 3.8

2.6 2.0 2.1

2,5,5 SNV 2,5,5 SNV 2,5,5 SNV

400–2500 400–750 1100–2500

149 161 159

7.4 7.9 7.9

0.95 0.66 0.83

1.6 2.9 3.2

0.85 0.50 0.71

3.3 6.8 4.1

2.4 1.2 2.0

1,5,5, None 2,5,5 None

400–2500 400–2500

139 155

7.6 7.8

0.93 0.92

1.9 2.1

0.75 0.75

4.0 4.0

2.0 2.0

S.D.: standard deviation in the calibration model; R2cal : coefficient of determination in calibration; SEC: standard error in calibration; R2cv : coefficient of determination in cross validation; n: number of the samples used to perform the calibration; SECV: standard error of cross validation, S.D./SECV: standard deviation/standard error of cross validation ratio.

Table 2 Calibration and cross validation statistics for oil on intact sunflower samples (g kg−1 dry weight) Wavelength segment (nm)

n

S.D.

R2cal

SEC

R2cv

SECV

S.D./SECV

1,5,5 SNV 1,5,5 SNV 1,5,5 SNV

400–2500 400–750 1100–2500

216 231 211

42.8 44.0 40.2

0.82 0.50 0.77

18.2 32.7 19.0

0.70 0.30 0.65

24.8 38.3 24.7

2.1 1.3 2.1

2,5,5 SNV 2,5,5 SNV 2,5,5 SNV

400–2500 400–750 1100–2500

219 218 220

43.5 40.2 42.8

0.90 0.60 0.82

15.4 26.5 18.1

0.80 0.50 0.75

22.3 30.3 23.6

2.3 1.7 2.2

1,5,5 None 2,5,5 None

400–2500 400–2500

225 217

43.3 42.5

0.76 0.86

21.2 16.0

0.60 0.75

27.7 22.3

1.9 2.3

S.D.: standard deviation in the calibration models; R2cal : coefficient of determination in calibration; SEC: standard error in calibration; R2cv : coefficient of determination in cross validation; n: number of the samples used to perform the calibration; SECV: standard error of cross validation, S.D./SECV: standard deviation/standard error of cross validation ratio.

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Table 4 Calibration and cross validation statistics for crude protein on intact sunflower samples (g kg−1 dry weight) Wavelength segment (nm)

n

S.D.

R2cal

SEC

R2cv

SECV

S.D./SECV

1,5,5 SNV 1,5,5 SNV 1,5,5 SNV

400–2500 400–750 1100–2500

141 144 137

35.1 35.5 34.6

0.90 0.65 0.90

11.4 20.9 11.1

0.82 0.60 0.82

15.0 23.3 14.8

3.4 2.1 3.5

2,5,5 SNV 2,5,5 SNV 2,5,5 SNV

400–2500 400–750 1100–2500

132 144 144

34.6 37.3 35.5

0.96 0.77 0.90

6.6 18.3 11.5

0.90 0.62 0.81

13.1 23.1 15.3

3.8 2.2 3.3

1,5,5 None 2,5,5 None

400–2500 400–2500

138 136

35.0 35.0

0.88 0.92

12.1 9.8

0.70 0.85

19.0 14.9

2.7 3.4

R2cal : coefficient of determination in calibration; SEC: standard error in calibration; R2cv : coefficient of determination in cross validation; n: number of the samples used to perform the calibration; SECV: standard error of cross validation, S.D./SECV: standard deviation/standard error of cross validation ratio. Table 5 Prediction statistics for oil, moisture, crude protein on intact sunflower samples using second derivative and Vis-NIR (g kg−1 dry weight)

M Oil CP

Mean

R2

SEP

Slope

Bias

67.0 461.4 297.9

0.70 0.65 0.72

3.4 23.2 17.1

0.91 0.97 0.97

0.05 −0.30 0.003

M: moisture; CP: crude protein; Mean: mean of the predicted values; R2 : coefficient of determination in prediction; SEP: standard error of prediction.

90

2

R = 0.70 -1 SEP = 3.4 g kg n = 37

85 -1

Moisture predicted by NIRS (g kg )

(Vis-NIR region). More than 30% of outlier samples were observed in both moisture and CP prediction. The performance of the calibration and cross validation models were evaluated the S.D./SECV ratio (see Tables 2–4). When the error in calibration (SEC or SECV) exceeds one-third of the S.D. of the population, regression models can be misleading (Murray, 1993; Fearn, 2002). In decreasing order of performance the results were for CP, M and oil, respectively.

80 75 70 65 60 55 50 50

55

60

65

70

75

80

85

90

-1

Moisture by reference (g kg ) Fig. 3. NIRS predicted values against measured reference values for moisture (second derivative, SNVD, Vis-NIR).

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R = 0.65 -1 SEP = 23.2 g kg n = 50

-1

Oil predicted by NIRS (g kg )

700 650 600 550 500 450 400 350 300 300

350

400

450

500

550

600

650

700

750

-1

Oil by reference (g kg ) Fig. 4. NIRS predicted values against measured reference values for oil (second derivative, SNVD, Vis-NIR).

450 2

R = 0.72 -1 SEP = 17.1 g kg n = 37

-1

CP predicted by NIRS ( g kg )

400 350 300 250 200 150 100 50 50

100

150

200

250

300

350

400

450

-1

CP by reference (g kg ) Fig. 5. NIRS predicted values against reference values for crude protein (second derivative, SNVD, Vis-NIR).

4. Discussion The concentration of oil, CP and M (Table 1) used to develop either the calibration or validation models, were found to be comparable to those re-

ported in the literature (Flagella et al., 2002). The NIR spectral bands found in this study are consistent with data reported by other authors (Murray, 1986; Panford and de Man, 1990; Sato et al., 1995; Pérez-Vich et al., 1998). Both the CH overtones and

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combinations bonds associated with the fatty acid carbon chain and cis-unsaturation in sunflower were the major spectral features observed. The inclusion of both Vis and NIR region improved the calibration models for the three quality parameters analysed, however either Vis or NIR regions separately did not give reliable calibration models. The best coefficient of determination in calibration (R2cal ) and the lowest standard error in cross validation were obtained after applying the second derivative and using the Vis-NIR region. For M the R2cal was 0.95 (SECV: 3.3); for CP was 0.96 (SECV: 13.1) and for oil was 0.90 (SECV: 22.3) in g kg−1 on a dry weight basis. The use of the first derivative as well as the log 1/R did not result in an improvement of the NIR calibration models. Overall, the calibration and cross validation results agreed with those reported by other authors (Robertson and Barton, 1984; Pérez-Vich et al., 1998). From the results obtained it is clear that NIR could be sensitive to variations in the colour of the individual achenes (from black to different strips). It is well known, that colour affects the degree to which light is reflected from the sample. In the case of very bright seeds some light may be reflected back for the sample and will not carry information to the instrument, on the other hand, very dark sample can cause bias at lower wavelengths (Williams, 2001). The presence of different colours (grey to black) and strips on the individual achenes could explain the poorest calibration obtained when the models were developed using the Vis region. The effect of colour on the calibration performance was reported by other authors where high NIRS correlation were obtained for husked samples compared with those obtained for intact samples (strips, different colours) (Robertson and Barton, 1984; Pérez-Vich et al., 1998). As well as colour, scanning achenes in a small cup ring could also affect the calibration models. It was reported also that bulk samples gave better NIR calibration models compared with single seed analysis (Williams, 2001). The use of large cuvettes (bulk samples) could be an alternative in order to improve the NIR calibration models. The results in this study showed that high and low oil content absolute values were not well predicted by the NIRS calibration models. High oil content as well as old samples (harvested in 1997 and 1998) had dif-

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ferent spectral position (high H values) compared with the rest of the samples (data not presented). Those samples were considered as outliers when both calibration and prediction models were developed. This indicated that not only the amount of oil in the sample (high or low) affect the calibration statistics for this quality parameter, but also the storage conditions of the sample as well as the year of harvest (e.g. humidity, temperature). Agronomic and environmental conditions affected the performance of NIRS calibration in cereals, as well as factors such as oven temperature, time of drying, and vaporisation of substances other than water and biochemical reactions (Dardenne et al., 1995). Storage of the sample without either control of temperature or humidity could explain the poorest prediction statistics for moisture. NIRS has proven that it is capable to produce repeatable results for breeding purposes. Although not high coefficients of determination in prediction were obtained for oil and M (R2cal < 0.70), the advantage of evaluating individual phenotypes in sunflower breeding for quality characteristics outweigh the losses of accuracy when many samples need to be selected (Fernandez-Martinez et al., 1993; Velasco et al., 1999). The level of accuracy obtained in this study is probably adequate for the selection of genotypes according to low, medium and high oil content, independently of the absolute value. Non-destructive analysis by NIRS allowed the breeder to improve selection based on quality characteristics. The moderate SD/SECV ratio obtained suggests that NIRS could be used to pre-screening for oil and CP on intact sunflower seeds. NIRS has been criticised because the errors associated with the predicted value for chemical composition. Errors are defined as the difference between the measured value and the true value. It is well known that all method have errors associated with them, including the traditional methods used for reference (Murray, 1996). Since NIRS is a predictive method, it inherently has built in errors associated with the reference method plus other errors that could occur (sample presentation, sampling, scatter effects, storage). Poor predictions could be expected in a population set that had little variation, however the reference method used could play a role. In a genetic or breeding program the major need is to separate the sources of variation. For other uses such as marketing

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and processing, a more precise estimation of seed oil content will needed. The continued inclusion of materials in the calibration database should provide greater accuracy in predicting seed oil content in the future. Our results show that NIRS can be used to estimate oil, CP and M content in intact sunflower seed samples. This has special relevance to plant breeding programs, where the analysis of a large number of entries (varieties) is often necessary in a short period of time. Although the SEP obtained for oil are considered high, the use of NIRS for intact sunflower samples could be used for pre-screening purposes, pre-select different varieties to be further analysed by reference methods if the absolute value is needed. NIRS would reduce the number of more expensive, destructive and time-consuming analysis such as Soxhlet extraction 5. Conclusions The results of this work demonstrate the use of NIRS to analyse intact sunflower seeds for oil, CP and M content. The accuracy obtained in calibration was sufficiently reliable for screening sunflower samples for breeding purposes. NIRS is a cost effective technique for plant breeding since it is very versatile, usually requires minimal sample preparation, permits a large number of samples to be analysed per day and may produce results for many traits simultaneously. Re-calibration may be necessary for M and oil, over different harvests. Especially, because these parameters are susceptible to change with climatic or agronomic conditions. Samples with different husk colour and/or strips must be included to increase the accuracy of the calibration models, especially for oil content. Acknowledgements The authors thank Ms. Patricia Gonzalez for the technical assistance. The work has been supported by INIA—Uruguay. Suggestions and comments by editorial reviewers are gratefully acknowledged. References AOAC, 1990. Official Methods of Analysis, 15th ed. Association of Official Analytical Chemists. Arlington, Virginia, USA.

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