acorns by Fourier transform - OSA Publishing

C. Sousa-Correia et al., J. Near Infrared Spectrosc. 15, 247–260 (2007)�

�� 247

Oil content estimation of individual kernels of Quercus ilex subsp. rotundifolia [(Lam) O. Schwarz] acorns by Fourier transform near infrared spectroscopy and partial least squares regression Cristina Sousa-Correia,a Ana Alves,b José C. Rodrigues,b,* Suzana Ferreira-Dias,c José M. Abreu,d Nigel Maxted,a Brian Ford-Lloyda and Manfred Schwanningere a

School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

Tropical Research Institute of Portugal (IICT), Forest and Forest Products Centre, Tapada da Ajuda, 1349-017 Lisboa, Portugal. E-mail: [email protected] b

Instituto Superior de Agronomia, DAIAT, Centro de Estudos Agro-Alimentares, Universidade Técnica de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal c

d

Secção Autónoma de Ciências Agrárias, Faculdade de Ciências, UP, Campus Agrário de Vairão, 4485-661 Vairão, Portugal

BOKU–University of Natural Resources and Applied Life Sciences, Vienna, Department of Chemistry, Muthgasse 18, A-1190 Vienna, Austria e

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak (Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure (n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm–1 and from 4478 to 4177 cm–1 of the vector normalised spectra, a PLSR model with a coefficient of determination (r2) of 0.992 and a root mean square error of cross-validation (RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled. Keywords: Fourier transform near infrared (FT-NIR) spectroscopy, Holm oak acorn, kernel, oil content, partial least squares regression (PLSR)

Introduction Holm oak [Q. ilex subsp. rotundifolia (Lam) O. Schwarz)] and cork oak (Quercus suber L.) are keystone species of traditional Mediterranean agricultural systems known as montados in Portugal and dehesas in Spain. These cover an area of about 1.1 million ha in Portugal,1 occupying critical

DOI: 10.1255/jnirs.733

areas in terms of soil and water resources, with low rainfall and high evaporative demand during summer. Their interlocking land-uses, forestry, agriculture and extensive grazing confer them the designation of agrosilvopastoral systems. In addition to their social and cultural importance, montados also have a high biological significance both in terms of wealth of biodiversity2 and a biological role in preventing

ISSN 0967-0335

© IM Publications 2007

248 �� Estimation of Oil Content in Individual Acorn Kernels

desertification in poor productive areas further threatened by climatic change.3 There has been a decline of these evergreen oak species during the last century as a result of deforestation, unfavourable climatic factors, pests and diseases and overgrazing that contributed substantially to prevent regeneration.1 The sustainable conservation of these agrosilvopastoral systems is dependent on the ability to integrate their ecological value with their economic potential for farmers and landowners. A strategy for achieving this includes a balance of several production factors, from which the increase in tree density of Q. ilex subsp. rotundifolia in montados, as well as the genetic improvement of the species for oil content, are considered very promising. The acorns of these evergreen oaks, once widely used for human consumption but nowadays mainly used as feed for free-range husbandry, are a source of high quality oil with a similar fatty acid composition to olive oil.4 The further exploitation of Holm oak oil (either as an edible oil or as a major complement to animal diet) could have a favourable impact on the economic viability of montado systems and thus contribute to the general welfare of the economically and socially disadvantaged region in Portugal. The actual annual estimated production capacity of acorns in Portugal is 300,000 tonnes.5 The oil content of these acorns is on average 9% with a large tree-to-tree variation (3–17%), assessed in pooled tree samplings.5 The traditional methods used to assess oil content in biological materials are based on time-consuming and expensive solvent extraction techniques that are unsuitable for the evaluation of large numbers of samples. NIR spectroscopy, a fast, non-destructive and accurate method is a natural candidate to handle this task. The near infrared (NIR) region spans the wavenumber range 12,800– 4000 cm–1, in which absorption bands occur as a result of molecular bond vibration that give rise to overtones and combinations of the fundamental vibrations active in the mid-infrared region.6 The main absorption bands observed in the NIR reflectance spectra arise from overtones and combinations of fundamental vibrations of C–O, O–H, C–H, N–H and S–H bonds. These molecules are the major chemical

moieties in virtually all biological samples. Various constituents give similar absorption signals leading to highly overlapping bands. On the other hand, some of them can be directly related to the chemical abundance of a “single” constituent or functional group.6 In addition to chemical information, NIR spectra contain physical information that can be used to determine physical properties, such as bulk density in seeds7 and seed weight.7 In NIR spectroscopy, the problem of multiple overlapping peaks can be handled through multivariate statistics and linear combinations of spectral data. Thereby, relationships with various chemical components have been shown and applied to assess oil content in seeds of Vernonia galamensis,8 flax seeds,9 rosehip seeds and hazelnut,10 rapeseed,11 maize grain, 12 olive fruit, 13–16 soybean, 17 sunflower, 18,19 groundnuts20 and leaves.21 Therefore, NIR should replace the traditional methods used to assess oil content22 in biological materials based on time-consuming and expensive solvent extraction techniques that are unsuitable for the evaluation of a large number of samples as in this case. For molecular markers association studies of maternal trees and the oil content of their acorns, focusing on the genetic improvement of Holm oak for oil content, a method for the prediction of the oil content of many samples would be very useful. Therefore, it was the aim of this work to investigate if NIR spectroscopy could be used as a rapid and inexpensive means to estimate the oil content of individual Holm oak acorn kernel samples taken from different trees and sites collected over two years.

Material and methods Plant material Acorns were collected�� during November�� of 2003 and 2004. The 2003 samples were stored at room temperature for about two months and then at –20°C, whereas the 2004 samples were stored at –20°C until use. In 2003, acorns were collected in three locations in the North, Centre and South of Portugal (Table 1). From 1 to 15 acorns were collected per tree (Table 1). A number painted on the trunk identified

Table 1. Sites and years of acorn collection.

Year

Site

2003

2004

No. of trees

No. of acorns

Frieira, Bragança

15

15

Lentiscais, Castelo Branco

28

138

Campo Maior, Portalegre

25

152

Alvito, Beja

10

�� 56

Lentiscais, Castelo Branco

14

�� 74

Campo Maior, Portalegre

24

124

S. Matias, Évora

10

�� 58

Moura, Beja

10

�� 50

C. Sousa-Correia et al., J. Near Infrared Spectrosc. 15, 247–260 (2007)

each tree in the field. In 2004, the acorns were collected from the same trees in the same locations in Castelo Branco and Portalegre (Table 1) and also in three additional locations in the South of Portugal: Moura, Beja and Évora. At least five acorns per tree were selected for analysis on the basis of integrity, good sanitary conditions and normal size (size extremes were excluded from analysis).

Sample preparation Acorns collected in November 2003 were dehulled and their kernels dried (60°C overnight) and milled (coffee mill) in April 2004 (98) and in January 2005 (207). Acorns collected in November 2004 were dehulled and their kernels dried (60°C overnight) and milled in January 2005 (362). An overview of the samples, their processing and the number of spectra collected at different temperatures by three people is given in Table 2. Additional drying experiments (not included in Table 2) with 28 selected samples from 2003 (14) and 2004 (14) were performed. The 28 samples were dried in an oven at 60°C overnight and the 14 samples from 2004 were dried again in an oven at 60°C twice overnight. Thereafter, the 14 samples from 2004 were dried again at 105°C for 15 h, followed by an additional drying for 28 h at 105°C. Spectra were collected each day.

Oil content determination A total of 89 milled samples (single kernel acorns) selected for oil extraction were air-conditioned followed by overnight drying at 60°C and finally at 105°C for 2 h. The samples dried at 105°C were extracted (by three people) with n-hexane using 250 mL flasks with a ground joint which fitted directly onto the condenser. The samples suspended through the condenser in the extraction thimbles were extracted in a two-step procedure: first, the samples were immersed in the boiling solvent for 30 min

249

( percolation step) followed by a 60 min period suspended above the boiling solvent (extraction step). After solvent removal, under reduced pressure, the extracts (oil) were dried overnight at 60°C, allowed to reach room temperature in a desiccator and weighed. The precision of the extraction procedure was assessed by combining five samples from each year with similar oil content. Each of these two composite samples, 2003 and 2004, was split into four aliquots (eight aliquots in total) of similar weight. The oil content was assessed over four different days using one aliquot from each year per day (two aliquots per day). These results were used for external validation.

Fourier transform (FT)-NIR spectroscopy FT-NIR spectra were recorded from 10,000 to 4,000 cm–1 (1000 to 2500 nm) with an 8 cm–1 spectral resolution and 50 scans per spectrum with a Bruker Vector 22/N spectro meter (Bruker Optics, �� Ettlingen, Germany) equipped with an integration sphere. Spectra of all samples were collected at room temperature after each drying step. After drying at the temperatures listed in Table 2, the samples were stored in �� a desiccator for temperature equalisation�� prior to the spectra being collected. The average spectra and their standard deviation spectra were offset corrected with the OPUS software (Bruker Optics, �� Ettlingen, Germany) ��23 using the minimum at about 5365 cm–1.

Calibration–validation Partial least-squares regression (PLSR) modelling OPUS Quant software was used for data pre-processing (vector normalisation), for the calculation of the PLSR models and for the prediction of the evaluation samples. Calibration models and validation of the models In a first step, the pre-processed (vector normalisation) NIR data were regressed against the calibration component

Table 2. Overview of the samples, processing and the number of spectra collected at different temperatures by three people. cal.: calibration; pred.: prediction, RT: room temperature.

Sampling

Milling and spectra collection

Samples for cal. / pred.

Person

Extraction

2003

Apr–04

17 / cal.

1

Apr–04

RT, 60, 105

51

2003

Apr–04

18 / cal.

2

May–04

RT, 60, 105

54

2003

Apr–04

63 / pred.

1

—

60

63

2003

Jan–05

18 / cal.

3

Aug–05

RT, 60

36

2003

Jan–05

189 / pred.

1

—

60

189

Total 2003

Drying temperature (°C) No. of spectra

305

393

2004

Jan–05

24 / cal.

1

Jan–05

60, 105

48

2004

Jan–05

12 / cal.

3

Aug–05

60

12

2004

Jan–05

326 / pred.

1

—

60

326

Total 2004

362

386


4260 cm–1, CH2 first overtones at 5770 cm–1 and 5670 cm–1, second overtone at about 8330 cm–1,6 and first overtone of combination bands from CH2 stretch and deformation at 7200 cm–1 and 7080 cm–1. The NIR spectrum of the acorn shows the CH2 combination bands [Figure 1(a) spectrum a, range 1] and the first overtone [Figure 1(a) spectrum a, range 2] especially evident in the samples with higher oil content. By comparison with the spectrum of the same sample after removing the oil [Figure 1(a) spectrum b] the contribution of the CH2 second overtone could be anticipated [Figure 1(a) spectrum a, range 3]. The difference spectrum [Figure 1(b) spectrum a], obtained by subtraction of these two spectra, reveals all the characteristic bands of the oil including the first overtone of combination bands from CH2 stretch and deformation [Figure 1(b) spectrum b, range 4]. The difference spectrum is very similar to the spectrum of the acorn oil [Figure 1(b) spectrum b]. In a first step, the pre-processed (vector normalisation) NIR data were regressed against the calibration component (oil content) and by means of full cross-validation with one sample omitted a significant number of PLS components was obtained. For this calibration the ranges 1 and 2 [Figure 1(b)] were selected and ranges 3 and 4 were not included.

(a)

0.8

0.5

a

0.4

3

log (1/Refl.)

0.6 2

b 0.3 0.2

9000

8000

7000

6000

0.1 4000

5000

-1

wavenumber [cm ]

1.4 1.2

1

(b)

absorbance

0.8 0.6 0.4

FT-NIR spectroscopy

0.2

0.135 0.115

2

1.0

Results and discussion Bands in NIR spectra overlap a great deal and an assignment to a functional group is often difficult. According to the literature, the major NIR absorption bands in a fat or oil are due to the long-chain, fatty acid moieties, which gives rise to the CH2 stretch bend combination at about 4330 cm–1 and

0.9

0.7

External validation Validation of the PLSR model obtained in the third step of the calibration was performed with the independent sample set selected to evaluate the precision (repeatability) of the oil extraction method and for the model evaluation. Outlier and outsider detection Mahalanobis distance is used as a diagnostic for outlier detection in multivariate calibration and also to detect those samples which would lead to an extrapolation of the model. For the calculation of the limit of the Mahalanobis distance, a factor of three was used. A more detailed description is given elsewhere.23,24 Spectra were classified as outsiders when the predicted value was outside the calibrated range, or classified as outliers if they were not similar enough to those in the calibration data set.

1

0.095 0.075

3

0.055

4

difference

and, by full cross-validation with one sample omitted, a significant number of PLS components was obtained.23,24 In a second step (test set validation), the calibration data set was divided into two groups [(a) and (b)]. After �� sorting the whole data set according to the oil content, the samples were chosen alternately for the calibration set and for the test set.�� Each group was used for both cross-validation (CV) and test set�� (TS)� �� validation. First, group (a) was used for CV and (b) for TS and then in reverse order, to evaluate if the model statistics were identical or at least very similar leading to the same rank. All models were calculated to a maximum rank of 10 and the results of the calibration [R2 coefficient of determination and root mean square error of estimation (RMSEE)], the cross-validation [r2 and root mean square error of cross-validation��(RMSECV)] and the test set validation [r2 and root mean square error of prediction� (RMSEP)] were compared. Therefore, test set validation was performed using the calibration with optimal rank in the cross-validation (as usual in an external validation) and also an optimal rank was defined through test set validation. The comparison of the ranks gives a first indication of the predictive ability of the model, because models with large differences between the ranks determined by CV and TS are never satisfactory.24 In a third step, a PLSR model including all calibration spectra was calculated for further investigations using an “independent” sample set for the external validation. The separately-prepared independent sample set was neither included in the calibration nor test set.

0.035

b

0.015

a

-0.005

0.0 9000 8500 8000 7500 7000 6500 6000 5500

-0.025 5000 4500 4000

-1

wavenumber [cm ]

Figure 1. (a) NIR spectra of acorn a and oil free acorn b and (b) the difference spectrum a and the acorn oil spectrum b.


First, this was because of the smaller contribution of the oil bands in ranges 3 and 4, with the spectral information of these bands already being included in the ranges used (1 and 2) and the expected decrease in signal-to-noise ratio with the increase in wavenumber. Nevertheless, the ranges 3 and 4, as well as the full spectrum, were also investigated because it has been shown24 that models can be improved. However, in this study no improvements were observed by the addition of ranges 3 and 4, as compared to the results obtained with�� only� the chosen ranges (1 and 2).

PLSR modelling PLSR models based on the oil content (2.5–17.3%) determined by person 1 (17 samples) and 2 (18 samples) (Table 2) using the wavenumber ranges 1 (5995 to 5323 cm–1) and 2 (4478 to 4177 cm–1) [Figure 1(a)] and the vector normalised spectra were calculated. The ranks (number of principal components) of these two models, as well as the model statistics, were very similar (Table 3, 1/1 and 2/2). As described in the material and methods section, each data set served as a test set for the validation of the other data set, leading to almost identical results. The combined data set that also gave similar results (Table 3, 3/3) was used for the prediction of the remaining samples with unknown oil content collected in 2003, resulting in only one outlier (OL) in 63 samples and one outsider (OS) and 20 OL in 189 samples (Table 3, 3/3 and 3/7). A new data set created by another person (3) about one year later was added and validated in the same way (Table 3, 4/4). This data set itself, as well as the inclusion of the already existing data sets, improved the predictive ability, resulting in no OS and a lower number of OL (Table 3, 4/4; 5/5; 4/8; 5/9). The models (3/3, 4/4 and 5/5) were used for the prediction of the oil content of the samples collected in 2004, giving 7 to 19 OS and 30 to 85 OL out of 326 samples (Table 3, 3/11; 4/12; 5/13). In 2004, new sites (Table 1) were included and the oil content in two sample sets (24 and 12 samples) covering the range from 2.8–18.9%, again extracted by different people, was determined. The calibrations based on these data (Table 4, 7/14; 8/15; 9/16) produced good calibration statistics as well as good results during prediction of the 326 samples from 2004. The number of OS was reduced to 1 and the number of OL to 3. The calibration, as well as the validation results presented in Figures 2(a)–(c), show that the slope of the regression lines was close to 1.0, the intercept close to zero and the coefficient of determination was close to 1.0 and that the samples were well distributed over the whole oil content range. A model based on all samples (89) with known oil content (2.5–18.9%) produced good calibration statistics (Table 3, 6/6) as well as good results during prediction of the 2003 (Table 3, 6/6 and 6/10) and 2004 (6/17) samples with low numbers of OS and OL. Moreover, the number of outliers removed during the cross-validation of all models (between 0 and 3) and the test set validation (between 0 and 2) was low (Table 3 and 4). The calibration and cross-validation results presented in Figures 2(d) and (e) show that the samples are

251

well distributed over the calibrated range with the regression line being almost a 1 : 1 line. Interestingly, the models based on the oil content determined in 2004 (sampling in 2003) (Table 3, 1/1; 2/2; 3/3) gave lower RMSEE than those obtained in 2005 (sampling in 2003 and 2004) (Table 3, 4/4 and Table 4, 7/14; 8/15; 9/16). These differences cannot be explained by the differences between the analysis results obtained by different people. RMSEE, based on 2003 samples processed in the same way, were very similar (Table 3, 1/1 and 2/2), also those from the 2004 samples (Table 4, 7/14 and 8/15) that were higher and the RMSEE of the 2003 samples analysed in 2005 lay in between (Table 3, 4/4). The average spectra of the 2003 and 2004 samples [Figure 3(a)] and especially the offset corrected average spectra [Figure 3(b)] revealed a difference in the moisture content of the samples, although the same processing methods were followed for all the samples. The standard deviation spectra [Figure 3(c)] of the 2003 samples analysed in 2004 (2003b), the 2003 samples analysed in 2005 (2003a) and the 2004 samples analysed in 2005 (2004) showed an increasing standard deviation of the moisture content (band at about 5180 cm–1), although the number of samples increased (almost doubled in each step). The spectra collected after drying at 105°C prior to oil determination also show a difference in the water content (not shown). The oil content was based on the dry weight of a sample. The differences in the moisture content already visible in the spectra of the material dried at 60°C (Figure 3) were reduced during drying at 105°C. However, the remaining differences still led to deviations in the oil determination, for example, higher water content–lower oil content. To take into account the variation in moisture content of the samples, especially between years, a correction of the determined oil content based on the moisture content would be needed. Therefore, the moisture content at room temperature and after drying at 60°C of 14 samples from 2003 and 14 from 2004 was determined. The spectra of these samples were collected after room temperature conditioning and after oven drying at 60°C and 105°C. By using the local baseline method, a line between the local minima at 5350 and 5020 cm–1 was drawn and the band height at 5187 cm–1 measured. The band height of the sample dried at 105°C was subtracted to obtain the band height differences. These differences were correlated with the moisture content (Figure 4).

Moisture content determination and correction of the oil content The band height difference was obtained by measuring the band height at 5187 cm–1 and subtracting the band height of the driest (�� dried at �� 105°C) calibration sample. The moisture content of the calibration samples, as well as the average spectra [Figures 3(a) and (b)], were calculated using this difference and the equation shown in Figure 4 and the oil content corrected accordingly. The estimated moisture content of the spectra shown in Figure 3(a) and (b) were 3.6% and 6.3%, respectively.

1

2

1, 2

3

1, 2, 3

1, 2, 3

1, 2

3

1, 2, 3

1, 2, 3

1, 2

3

1, 2, 3

1/1

2/2

3/3

4/4

5/5

6/6

3/7

4/8

5/9

6/10

3/11

4/12

5/13

x

x

x

x

x

x

x

x

x

x

x

x

x

2003

x

x

2004

53

18

35

89

53

18

35

89

53

18

35

18

17

No.

03/04

03

03

03/04

03

03

03

03/04

03

03

03

03

03

3

2

2

4

3

2

2

4

3

2

2

2

2

R

0.37

0.47

0.31

0.43

0.37

0.47

0.31

0.43

0.37

0.47

0.31

0.30

0.22

RMSEE (%)

0.99

0.98

0.993

0.99

0.99

0.98

0.993

0.99

0.99

0.98

0.993

0.98

0.995

R2

0.42

0.52

0.35

0.46

0.42

0.52

0.35

0.46

0.42

0.52

0.35

0.37

0.27

RMSECV (%)

0.98

0.97

0.99

0.99

0.98

0.97

0.99

0.99

0.98

0.97

0.99

0.97

0.991

r2

Cross validation

2

1

3

2

2

1

3

2

2

1

3

2

0

OL

—

—

—

—

—

—

—

—

—

4d

2c

2b

2a

R

—

—

—

—

—

—

—

—

—

0.46

0.35

0.43

0.59

RMSEP (%)

—

—

—

—

—

—

—

—

—

0.98

0.99

0.98

0.96

r2

Test set validation

—

—

—

—

—

—

—

—

—

1

0

0

2

OL

04

04

04

03

03

03

03

03

03

03

03

03

03

326

326

326

189

189

189

189

63

63

63

63

63

63

No.

d

a

7

18

19

0

0

0

1

0

0

0

0

8

1

OS

56

85

30

3

18

5

20

0

0

2

1

0

2

OL

Prediction of unknown samples

The 18 samples from person 2 served as a test set. bThe 17 samples from person 1 served as a test set. cThe 18 samples from person 3 served as a test set. The 35 samples from person 1 and 2 served as a test set.

Person

Model No. / row

Calibration Sampling year

Calibration samples Sampling year

Table 3. Calibration and validation results of the calculated models and their predictive abilities. R: Rank (number of principal components); OL: outliers; OS: outsiders


1

3

1, 3

1, 2, 3

1, 2, 3

1, 2, 3

1, 2, 3

1, 2, 3

1, 2, 3

1, 2, 3

�� 8/15

�� 9/16

�� 6/17

�� 6/18

10/19

11/20

12/21

11/22

11/23

Person

�� 7/14

Model No./row

x

x

x

x

x

x

x

2003

x

x

x

x

x

x

x

x

x

x

2004

183

183

183

183

89

89

89

36

12

24

No.

03/04

03/04

03/04

03/04

03/04

03/04

03/04

04

04

04

7

7

9

9

4

4

4

3

2

2

R

0.32

0.38

0.33

0.34

0.47

0.43

0.43

0.54

0.52

0.65

RMSEE (%)

Calibration

0.994

0.992

0.993

0.993

0.99

0.99

0.99

0.99

0.992

0.98

R2

0.38

0.45

0.36

0.37

0.49

0.46

0.46

0.61

0.72

0.73

RMSECV (%)

�� 0.991

0.99

0.991

�� 0.992

0.99

0.99

0.99

0.98

0.98

0.98

r2

Cross validation

�8

3

15

11

�4

�2

�2

�2

�0

�1

OL

7

7

—

—

—

—

—

—

—

—

R

0.40

0.37

—

—

—

—

—

—

—

—

RMSEP (%)

0.99

0.992

—

—

—

—

—

—

—

—

r2

Test set validation

6

8

—

—

—

—

—

—

—

—

OL

—

—

03/04

03/04

03/04

03/04

04

04

04

04

—

—

578

578

578

578

326

326

326

326

No.

—

—

2

0

1

1

1

1

0

5

OS

—

—

18

14

17

18

15

5

9

3

OL

Prediction of unknown samples Sampling year

Calibration samples Sampling year

Table 4. Calibration and validation results of the calculated models and their predictive abilities, including the final models based on the corrected oil contents. R: Rank (number of principal components); OL: outliers; OS: outsiders.

C. Sousa-Correia et al., J. Near Infrared Spectrosc. 15, 247–260 (2007) 253


20

20 y = 0.9885x + 0.1343

18

2

R = 0.99

16

NIR oil content (Cal) [%]vv

NIR oil content (Cal) [%]vv

18

14 12 10 8 6 4

(a)

2

14 12 10 8 6 4

(d)

0 0

2

4

6

8

10

12

14

16

18

20

0

True oil content [%]

20

14 12 10 8 6 4

(b)

2

4

6

8

10

12

14

16

18

20


18

NIR oil content (CV) [%]vv

16

2

20

y = 0.9901x + 0.1125 2 r = 0.98

18

NIR oil content (CV) [%]vv

2

R = 0.99

2

0

y = 0.989x + 0.1209 2 r = 0.99

16 14 12 10 8 6 4

(e)

2 0

0 0

2

4

6

8

10

12

14

16

18

20

18

0

2

4

6

8

10

12

14

16

18

20



20

NIR oil content (TS) [%]vv

y = 0.9887x + 0.1281

16

y = 1.0202x - 0.0107 2

r = 0.98

16 14 12 10 8 6 4 2

(c)

0 0

2

4

6

8

10

12

14

16

18

20


Figure 2. (a) calibration (cal) and (b) cross-validation (CV) results of the 2003 samples and (c) the test set validation (TS) with the 2004 samples, (d) calibration and (e) cross-validation (CV) results of all samples (2003 and 2004).

PLSR models based on corrected oil content The model statistics obtained with the oil content corrected for differences in the moisture content and those obtained with the uncorrected data were almost identical (Table 4, 6/18 and 10/19). The number of OS was identical and the number of OL was reduced by 6%. The calculated moisture content of the calibration samples was in the range between 2.27% and 9.03%, with an

average of 4.56% for all, 3.48% for 2003 and 6.15% for 2004, which is very similar to the average of all samples of these years, 3.6% and 6.3% [Figures 3(a) and (b)]. The oil content corrected (uncorrected) due to the differences in the moisture content was in the range between 2.4% (2.3%) and 20.2% (18.9%) with an average of 11.7% (11.2%). The correction resulted in an average increase of the oil content of 0.5%.


255

12

0.9

(a)

0.8

0.5 0.4

2003

Water content [%]

0.6

log (1/Refl.)

0.7

2004

y = 115.73x - 2.7255 R2 = 0.85

10 8 6 4

0.3

2 0.2

9000

8500

8000

7500

7000

6500

6000

-1

5500

5000

4500

0

0.1 4000

0

wavenumber [cm ]

0.05

0.1

Band height difference at 5187 cm

Figure 4. The relationship between the water content in the 24 samples (74 spectra are shown due to the different temperatures) and the band height difference in the spectra measured at 5187 cm–1.

0.50

(b)

0.15 -1

0.45 0.40

0.30 0.25 0.20

2004

log (1/Refl.)

0.35

0.15 0.10 0.05

2003 6000

5800

5600

5400

5200

5000

4800

-1

4600

4400

4200

0.00 4000

wavenumber [cm ] 0.016

(c)

0.014

2004

0.010 0.008

2003a

0.006 2003b

STDEV log (1/Refl.)

0.012

0.004 0.002

5400

5300

5200

5100

5000

-1

4900

0.000 4800

wavenumber [cm ]

Figure 3. (a) Average spectra of the 2003 and 2004 samples. The spectra shown in (b) and (c) were offset corrected to the minimum at about 5365 cm–1. (b) The average spectra of the 2003 samples analysed in 2005 it is not show in (a) and (b) because it is almost identical to that of the 2003 samples analysed in 2004. (c) standard deviation spectra of the 2003 samples analysed in 2004 (2003b), of the 2003 samples analysed in 2005 (2003a), and the 2004 samples.

Predicting the oil content from the average spectra of the samples with unknown oil content from 2003 and 2004 with models 6/18 and 10/19 (Table 4) resulted in an increase in

the average oil content of 0.5% for 2003 (11.8% to 12.3%) and of 0.8% for 2004 (11.6% to 12.4%).

PLSR models calculated with additional spectra It is well known that an increasing moisture content not only results in an increase of the water bands at about 5180 cm–1 (O–H stretch, first overtone) and 6900 cm–1 (O–H stretch + O–H deformation, combination band). Thygesen25 reported that the reflectance spectra of wet wood shavings differed markedly from the spectra of the other wood samples. The reason is that the wet samples were gleaming with moisture. This dominated their scattering properties. Also, the influence of the temperature (thermal effects) on the NIR spectra has been investigated, resulting in shifting water bands26,27 as well as differences in the spectra of pure water.28 How water is bound in cells is still a matter of debate, but there can be no doubt that hydrogen bonding to hydroxyl groups is involved. Furthermore, it is a known fact for water that a decrease in temperature increases the average number of hydrogen bonds per OH group, which in turn shifts hydroxyl bands towards lower wavenumbers.26,27,29 Therefore, temperature variations can also be expected to give band shifts of the hydroxyl bands for a moist material. While it is easy to keep the temperature in an acceptable range, it is more difficult to handle variations in moisture content, mainly because these variations have an additional influence on the shape of the spectra.30 In addition, the two water bands inherent in the spectra are very different in “magnitude”. This is so because the water band that appears at approximately �� 5180 cm–1 is a narrow band with high absorbances and is very “sensitive” in the sense that small variations may also greatly influence the models that include wave-number ranges in the neighbourhood. These effects are not taking place with this magnitude in the other water band that appears �� at �� approximately 6900 cm–1.30,31 The influence of both surface and bound water on the spectra is well known in pharmaceutical sciences31,32 as well as food sci-


ence33–36 or for seeds37 leading to some typical complications in the analysis.38,39 Moreover, the water absorption/desorption behaviour is different40 and thus, it is difficult to obtain acorn samples with identical moisture content. Although the samples were identically processed, different moisture contents were attained. Fourteen samples from each year were selected. These 28 samples were dried in an oven at 60°C overnight and the 14 samples from 2004 were dried again in an oven at 60°C twice overnight and the spectra collected each day. The moisture content decreased, but a difference between the 2003 (one overnight) and 2004 samples remained (three overnights). The drying time was increased up to times that used in previous studies analysing, for example olive pomace dried for 15 h at 105°C,16 and olive fruits dried for 42 h at 105°C.15 Therefore, the acorn kernel samples were dried at 105°C for 15 h and even after an additional 28-hour drying at 105°C, differences in the water bands of the spectra remained. Due to the long dryingtime, a colour change of some samples was observed and some of them developed a rancid smell. Therefore, drying to an identical moisture content and/or constant weight was not feasible. To take the varying moisture content into account, the calibration sample spectra collected after storage at room

conditions and after drying at 105°C (Table 2) were included in the calibration data set (183 spectra). The resulting model statistics (Table 4, 11/20) show that the number of principal components and the r2 increased but the RMSECV decreased from 0.49% (Table 4, 10/19) to 0.37% (Table 4, 11/20). Additionally, no sample was qualified as an OS and the number of OL was reduced by 23%. This shows that the predictive power of the model improved. Then the wave number range 1 (5995 to 5323 cm–1) was enlarged (5995 to 4953 cm–1) to include the water band at about 5187 cm–1. The resulting model statistics (Table 4 12/21) are similar to those of the previous one (Table 4 11/20). However, two of 578 samples were predicted as OS and the number of OL increased.

Precision of the extraction method The standard deviation for the 2003 composite samples was 0.09% and for the 2004 samples 0.11% (Table 5). The oil content was water corrected as described above. As expected, the deviation for the 2004 samples was larger due to the higher moisture content. These samples were also used to find the final PLSR model for further prediction. The prediction results of the four models revealed that models 11/20 and 12/21

Table 5. Oil content of the composite samples determined by extraction and prediction with four PLSR models.

Sample name

Day Determined oil content (%)

Correcteda oil content (%)

Prediction of the oil content (%) Model

Model

Model Model

6/18

10/19

10/19

12/21

Composite2003_01

1

11.0

11.3

11.2

11.6

11.5

11.6

Composite2003_02

2

10.9

11.3

10.9

11.1

11.4

11.4

Composite2003_03

3

11.0

11.4

11.2

11.5

11.5

11.6

Composite2003_04

4

11.1

11.5

11.2

11.6

11.5

11.6

Composite2004_01

1

8.8

�� 9.3

�� 8.6

�� 9.2

�� 9.4

9.5

Composite2004_02

2

�� 9.0

�� 9.6

�� 8.8

�� 9.4

�� 9.6

9.8

Composite2004_03

3

�� 9.0

�� 9.5

�� 8.7

�� 9.3

�� 9.5

9.6

Composite2004_04

4

�� 9.1

�� 9.6

�� 8.5

�� 9.1

�� 9.4

9.5

Av.

11.02

11.36

11.12

11.46

11.47

11.53

Std dev.

�� 0.09

�� 0.09

�� 0.18

�� 0.21

�� 0.05

0.11

Av.

�� 8.96

�� 9.49

�� 8.65

�� 9.26

�� 9.47

9.60

Std dev.

�� 0.11

�� 0.12

�� 0.11

�� 0.12

�� 0.10

0.11

Composite2003

Composite2004

The moisture content was determined from the spectra and the oil content corrected as described in the section “Moisture content determination and correction of the oil content” a


gave the best results (Table 5), whereas model 12/21 gave higher values. This was also observed during the prediction of the 578 samples with unknown oil content. The average oil content obtained for these samples were 12.22% (model 11/20) and 12.28% (model 12/21). The composite sample values predicted with model 11/20 were nearest to the determined ones, additionally show-

ing the lowest standard deviations (Table 5). This model, subsequently called “final model” was, therefore, selected for further prediction.

Validation of the selected model The data set of the final model (183 spectra) was split into two groups (a) 92 calibration/cross-validation spectra and (b) 91

22

22

y = 0.9927x + 0.0816 2 r = 0.99

18 16 14 12 10 8 6 4

(a)

2

2

r = 0.991

18 16 14 12 10 8 6 4

(d)

2

0

0

0

2

4

6

8

10

12

14

16

18

20

22

0


22

16 14 12 10 8 6 4

(b)

2

4

6

8

10

12

14

16

18

20

22

True oil content [%] y = 0.9869x + 0.1584 2 r = 0.99

20

NIR oil content (TS1) [%]vv

18

2

22

y = 0.9751x + 0.3319 2 r = 0.992

20

NIR oil content (TS2) [%]vv

y = 0.994x + 0.071

20

NIR oil content (CV2) [%]vv

NIR oil content (CV1) [%]vv

20

18 16 14 12 10 8 6 4

(e)

2 0

0 0

2

4

6

8

10

12

14

16

18

20

22

0

2

4

6

8

10 12 14 16 18 20 22



22

y = 0.9919x + 0.0979 2 r = 0.991

20

NIR oil content (CVall) [%]vv

257

18 16 14 12 10 8 6 4

(c)

2 0 0

2

4

6

8

10 12 14 16 18 20 22


Figure 5. (a) Cross-validation (CV1) with group (a), (b) the test set validation (TS2) with group (b), (c) cross-validation (CVall) with all samples, (d) cross-validation (CV2) with group (b), and (e) the test set validation (TS1) with group (a) results of the 2003 and 2004 samples including the spectra of the calibration samples dried at room temperature and 105°C.


Table 6. Moisture content of the calibration samples and oil content of the calibration samples and the predicted samples from 2003 and 2004.

Moisture content (%)

Oil content (%) Determined

Predicted

Site

Year

Range

Average

Range

Average

Range

Average

Castelo Branco

2003

4.2–12.2

3.5

4.1–16.2

12.2

6.4–16.9

12.0

Portalegre

2003

4.4–12.0

3.5

�� 2.5–17.3

12.0

8.4–16.0

12.3

Macedo de Cavaleiros

2003

3.3–12.3

3.1

10.6–13.3

12.3

—

—

Alvito, Beja

2004

7.1–13.1

6.0

�� 6.6–16.5

13.1

3.9–18.2

12.8

Castelo Branco

2004

7.2–10.8

6.4

�� 5.4–17.2

10.8

5.9–18.0

13.7

Portalegre

2004

8.0–10.1

6.2

�� 2.8–18.9

10.1

3.6–19.8

12.4

Évora

2004

8.8–10.0

5.8

�� 2.3–18.0

10.0

2.7–19.0

10.1

Moura, Beja

2004

9.0–12.3

7.3

�� 9.5–14.5

12.3

4.1–17.1

10.9

test set spectra. The results of the calibration/cross-validation and test set validation are almost identical (Figure 5), even after using (b) for calibration/cross-validation and (a) for test set validation (Table 4, 11/22 and 11/23). The comparison of the ranks gives a first indication of the predictive ability of the model, because models with large differences between the ranks determined by CV and TS are never satisfactory.24 These and the above results confirm that model 11/20 [Figure 5(c)] shows good predictive ability.

Predicted oil contents of the�� 2003 and 2004� samples The oil content of the unknown individual acorns from 2003 and 2004 were predicted with model 11/20. The ranges and the average values presented in Table 6 show an increase in the oil content in 2004. The number of samples from site Frieira (Bragança) was too small to draw conclusions according to this site. Except for Portalegre 2003 and Évora 2004 also the ranges were similar. The latter site showed the lowest average oil content. Moreover, no simple relationship between oil content and site, as well as between oil content and water content, could be observed. Although a direct comparison to other published works is difficult due to different material, sample treatment, oil content and moisture content, the RMSEP and the residual prediction deviation (RPD) were used as a measure. The oil content (5.9–28.9%) determined from spectra directly collected from intact olive fruit samples gave a RMSEP between 0.84 and 2.41% depending on the sampling year.15 The latter was obtained when the calibration, based on samples from 1997 was validated with the samples collected in 1996, which additionally showed a bias of –0.91%. Prediction models for oil content (0–22%) of Rosa mosqueta and Chilean hazelnut were obtained with a standard error of cross-validation of 1.25%.10 From the analytical point of view in accordance with AACC Method 39–00,41 the RPD should be in the following range: ≥ 2.5 screening in breeding

programs; ≥ 5 acceptable for quality control, ≥ 8 good for process control, development and applied research.41 Oil content (40.3–53.6%) determination of groundnuts kernels with spectra collected in transmission mode using 14 wavelengths for the prediction model, an RPD of about five was obtained.20 The oil content in olive pomace (1.74–3.93%) could be determined with an RPD ≈ 2.4,16 and from olive fruits the best model obtained for oil content (18.9–28.8%) showed an RPD of 2.57.42 The latter is similar to the RPD (2.55) found for the oil content of peanuts.43 The values of the RMSECV and the RMSEP obtained in our study (Table 3 and 4) are all lower than to those found in the cited literature. The RPD values for model 11/22 and 11/23 (Table 4) are 10.8 and 9.8, respectively, with bias of –0.03 and –0.00058. These results confirm that the developed models are good for process control, development and applied research.

Conclusion Oil content estimation of Quercus sp. acorns by FT-NIR and PLSR was shown to be consistently precise and the varying moisture content within, and especially between, the years does not invalidate results. The results of the analyses performed by three people revealed good manageability of the extraction procedure and the precision (repeatability) of this method assessed during a four-day period was excellent with a standard deviation of 0.1%. Careful wavenumber selection and validation of the models led to the final model that allowed the precise prediction of samples with unknown oil content.

Acknowledgements This work was supported by a PhD research grant to the first author by the Portuguese Foundation for Science


and Technology (FCT, Portugal) SFRH/BD/ 12873/ 2003, a research grant for the second author from FCT, under POCTI and FEDER programmes POCTI/ AGR/47353/2002 project and was integrated in the activities of BIOPOL in Centro de Estudos Florestais (Portugal).

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