Prediction of the optimal picking date of different apple
cultivars by means of VIS
/
NIR-spectroscopy
Ann Peirs
a,*, Jeroen Lammertyn
a, Kristien Ooms
b, Bart M. Nicolaı¨
a aFlanders Centre of Posthar6est Technology/Laboratory of Posthar6est Technology,Willem de Croylaan42,3001Leu6en,Belgium
bCQ Consultancy,Inno
6atie-en Incubatiecentrum,Kapeldreef60,3001Leu6en,Belgium
Received 17 November 1999; accepted 12 July 2000
Abstract
The use of visible/near infrared (VIS/NIR) spectroscopy was evaluated to determine the internal quality and the optimal harvest dates of apples non-destructively. Calibration models were constructed with data from eight cultivars, three orchards and 2 years, in order to make the models as robust as possible for future use. The prediction of the maturity, defined as the number of days before commercial harvest, was reasonably accurate. The most robust model predicted the maturity with a validation correlation of 0.90 (SEP=7.4 days). The prediction of maturity, according to the Streif index, showed a validation correlation of 0.84 (SEP=0.18 kg/% brix×starch index) for one orchard. Maturity was orchard-dependent, however, and as a consequence, a combined prediction equation was not accurate. Individual quality characteristics (soluble solids, Streif index, acidity and firmness) were well predicted. The calibration model for soluble solids content resulted in a validation correlation of 0.84 (SEP=0.73% brix) for the results over 2 years from one orchard, but like the Streif index, was orchard-dependent and appeared to account largely for the orchard dependence of the latter. Acidity and firmness were predicted with a validation correlation of 0.80 and 0.78 and SEPs of 2.07 ml NaOH and 1.13 kg for, respectively, two and three orchards over the 2 years. © 2001 Elsevier Science B.V. All rights reserved.
Keywords:VIS/NIR-spectroscopy; Streif index; Apple
www.elsevier.com/locate/postharvbio
1. Introduction
To ensure a long-term storage potential of ap-ples (\6 months), it is essential that the fruits are
harvested within a well defined optimal harvest period. During this period, the respiratory activity
of the fruits is at its minimal and during subse-quent storage, controlled conditions are used to minimise respiratory and quality losses. If apples are harvested too early, they will not ripen suffi-ciently upon removal from storage and will have inferior organoleptic quality. In addition, early harvest increases the risk of superficial scald de-velopment, an important storage disorder. Con-versely, if the apples are harvested too late, they will soften and become mealy before or during
* Corresponding author. Tel.: +32-16-322668; fax: + 32-16-322955.
E-mail address:[email protected] (A. Peirs).
subsequent marketing. Even optimum storage conditions can not compensate for losses in stor-age potential due to the improper timing of har-vest (Skrynski, 1996).
If the optimal harvest period could be predicted well prior to harvest, it would also allow the grower to maximise harvest labour use efficiency. Two systems are currently used to predict the optimum date for harvest. The first method utilises meteorological parameters and the num-ber of days after full bloom (Luton and Hamer, 1983). These models are generally inexpensive and relatively easy to use, but require a relatively long history of meteorological and physiological obser-vations. Their primary deficiency is in the preci-sion of prediction during normal production years, which is even poorer during years with atypical weather conditions.
The second method is based on the temporal pattern of changes in individual or multiple chem-ical and physchem-ical properties of the fruit during a well defined period before harvest (Truter et al., 1985). In addition to firmness, ethylene produc-tion, stage of starch transiproduc-tion, colour, compo-nents of taste (e.g. sugar content, acidity) and aroma (e.g. esters and alcohols) are important indicators of maturity (Lal Kaushal and Sharma, 1995). Streif (1983) developed a prediction method based on eight fruit quality attributes which were later simplified to three (Streif, 1996). The Streif index is a combination of firmness (F), soluble solids content (R) and starch stage (S)
Index= F (kg)
R (%brix)S
The Streif index decreases during maturation of the fruit until reaching the threshold value for harvest (e.g. 0.08 for ‘Jonagold’ fruit in Belgium). At this point, fruit destined for extended storage must be harvested; a technique that is used by commercial growers in Belgium.
In the following study, data on fruit size, ground and background colour, soluble solids content, acidity, firmness and stage of starch tran-sition were collected weekly from different or-chards starting in mid-July. The time course of
these maturity indices was compared with
threshold values and previous data, and predicted
harvest dates were calculated for each cultivar. Due to the large number of samples required for the accurate prediction of harvest date (i.e. eight fruit per cultivar per week), a rapid, non-destruc-tive method is needed.
Since the late 1980s, near infrared (NIR)-spec-troscopy has been evaluated for measuring the internal composition of biological materials (Kays, 1999). The advantages of this technology are, (1) speed of measurement; (2) multiple at-tributes can be measured simultaneously; and (3) since it is non-destructive, repeated measurements could be made on the same sample. The latter increases the accuracy in that measurements of quality attributes of individual fruit can be made during development while still attached to the tree.
Soluble solids have been measured in a wide range of fruit using NIR spectroscopy (Kays, 1999). Kawano (1994), Kawano et al. (1992) de-veloped a linear model, using four wavelengths, which had a SEP of 0.50% brix. In contrast, McGlone and Kawano (1998) used the entire wavelength region between 400 and 1100 nm to generate partial least squares (PLS) models for the prediction of the firmness, dry matter, and soluble solids of kiwifruit (7.8 N, 0.42 and 0.39% brix, respectively). For tomatoes, a good correlation was also found between spectral data and soluble solids (i.e. SEP=0.69% brix, Ruiz-Altisent and Barreiro, 1996). NIR in the 850 – 1300 cm−1
re-gion has also been used to predict individual sugars, ethanol, and glycerol. The SEP values were 2.45 glucose g l−1 and 3.86 fructose g l−1.
Table 1
The number of measuring sessions (weekly) per cultivar, year and orchard
Year Orchard Jonagold Elstar Boskoop Golden Delicious Cox’s Orange Pippin Gala Braeburn
7 7
1997 Velm 7 6 5 – –
8 7 6 7
Velm 6
1998 5 8
Rillaar
1998 8 6 7 7 – – 4
2 2 2 2 – –
1998 Lendelede –
The objective of this study was two-fold, (i) to evaluate the potential to measure the internal quality characteristics of apples by VIS/ NIR-spec-troscopy in the preclimacteric phase; and (ii) to predict the optimal harvest date based on VIS/ NIR spectra. A special attempt was made to establish sufficiently robust calibration models that are applicable to all the different cultivars over different years.
2. Materials and methods
2.1. Fruit
The experiment was performed over 2 years (1997 and 1998). The apples were picked during a maximum of 8 weeks before the commercial pick-ing date at the experimental stations ‘Nationale Proeftuin voor Grootfruit’, in Velm, Belgium, and ‘Fruitteeltcentrum’ in Rillaar, Belgium; and at a commercial orchard in Lendelede, Belgium. These orchards are located in scattered locations throughout Belgium. Because of this, the soil types differ from sandy loam (Lendelede and Ril-laar) to loam (Velm). This set-up was chosen in order to create robust models without orchard or year effects. The experimental schedule is sum-marised for each orchard, cultivar and season in Table 1. For every measurement session, eight apples per cultivar were randomly picked at eye level, in the middle of the canopy of eight differ-ent trees. The apples were transported to the laboratory and stored under ambient conditions prior to measurement. All measurements were carried out on the same day or the day after picking. In total 952 apples were collected.
2.2. Quality parameters
The firmness was measured with a Magness – Taylor penetrometer with an 11-mm diameter plunger. The juice that was released by the firm-ness test was used to measure the soluble solids with a digital refractometer (Atago Co. Ltd., Tokyo, Japan). The acidity was determined through titration of 10 ml apple juice mixture (in groups of two or three apples) with 0.1 N NaOH to a pH of 8.8 (phenolphthalein).
2.3. Collection of VIS/NIR spectra
From each apple, four reflection spectra (380 – 2000 nm, wavelength increment 0.5 nm) were taken at four equidistant positions along the equator with a spectrophotometer (Optical Spec-trum Analyser (OSA) 6602, Rees InsSpec-truments Ltd., Goldalming, UK) in a 0/45° configuration (Fig. 1).
The bundled detecting fibres and the bundled source fibres were placed on a black holder (type 6151) under an angle of 45°. The light source (Dual light source, type 6290) consisted of a 12 V/100 W tungsten halogen lamp (type Philips
7724.M/28). This source is usable in the visible and infrared region. The reflected light is captured by a grating monochromator. The spectrum (380 – 2000 nm) was collected into two parts. A Si-detector (type 6611, 380 – 1080 nm) and an InGaAs-detector (type 6614, 1080 – 2000 nm) each measure one part of the whole spectrum and those two spectra are connected at 1080 nm. Every spectrum was divided by a reference spectrum taken from a BaSO4-plate to minimise the light
source ageing. Each measured reflection spectrum was the average of five individual optical scans from 380 to 2000 by 0.5 nm increments (Rees Instruments, Macam Photometrics Ltd., UK). Lammertyn et al. (2000) found that NIR light with a wavelength between 380 and 2000 nm penetrated into the apple flesh for up to 1 cm depending on the wavelength.
2.4. Data analysis
The averaged reflection spectra were analysed with the statistical program for multivariate cali-bration The Unscrambler (CAMO AS, Trond-heim, Norway). The spectrum was pre-processed first by calculating a three points segment moving average. After normalisation of the data, the number of measuring points was reduced 14 times (7 nm increments). The second derivative spectra were calculated using the method of Savitzky and Golay (1964) to correct for additive and multi-plicative effects in the spectra (Martens and Naes, 1987). The pre-processed data were used in the statistical analysis together with the quality parameters. The technique used was PLS. This is a projection method, like PCR, but uses both the independent and dependent variables to find the regression model with its PLS components. PLS often needs fewer latent variables to reach the optimal solution because the focus is on the de-pendent variables (De Jong, 1993). Cross valida-tion (in groups of 20 samples) was used to validate the models. Extreme outliers were re-moved from the data set. The accuracy of the calibration and validation are defined by SEC and SEP, as follows:
withyˆi, the predicted value of theith observation;
yi, the measured value of the ith observation; Ic,
the number of observations in the calibration set; Ip, the number of observations in the validation
set and bias=1/Ip iI=p 1(yˆi−yi).
3. Results
3.1. Influence of the maturation on the reflectance
spectra
The influence of maturation is shown in Fig. 2. It indicates the wavelength regions that are sensi-tive to the physiological changes of the apple before the harvest. In the visible region the up-ward shift of the spectra is due to the transition from a green to red skin colour. The shifts of the spectra in the NIR range are caused by changes in the internal absorption and scattering, which are related to changes in physicochemical properties of the fruits.
3.2. Prediction of the optimal har6est date
Fig. 2. Influence of maturity on the reflection spectra of a ‘Golden Delicious’ apple (arrows indicate increasing maturity). Table 2
PLS-model for Streif index
Model 1 2 3 4 5 6
1998 1998 1998
1997 1997–1998
Year 1997–1998a
Orchards included Velm Velm Rillaar Velm+Rillaar Velm Velm
380–2000 380–2000 380–2000 380–2000
380–2000 380–2000
Spectrum range (nm) Number of samples
376
In calibration 244 256 632 620 520
376 256 632
244 620
In validation 100
0.91
Calibration correlation 0.93 0.91 0.83 0.87 0.84
0.90 0.87 0.79
0.87 0.84
Validation correlation 0.85
9 7 10
cPLS components 8 10 9
0.11 0.12 0.18
0.15 0.16
SEC 0.19
0.14 0.15 0.20 0.18 0.17
SEP 0.17
aModel with external validation set.
models are based on the data from the same orchard (Velm) but for different years. The SEP values do not change markedly (increase from 0.17 (model 1) to 0.18 (model 5)). There is a little loss of accuracy caused by combining the 2 years, but this is acceptable as the robustness of the model increases. The orchard dependence of the Streif index is related to the orchard dependence of the soluble solids content (see further). To test whether the model can be used for new data, an external validation set of 100 samples was chosen
randomly. The other 520 samples were used to establish the calibration model (model 6). Based on the external validation set, an acceptable vali-dation correlation of 0.85 was obtained; the SEP was 0.17.
the Flanders Centre of Postharvest Technology. This period is in general one to 2 weeks wide and is predicted based on a comparison of the time course of soluble solids content, acidity,
back-ground colour, size and firmness with historical data. In Table 3, the PLS models for the predic-tion of the number of days before picking date are shown. The models show that the maturity of the
Fig. 3. Calibration model for maturity (number of days before the optimal harvest date) for a combination of seven cultivars, three orchards over a 2-year period.
Table 3
PLS models for maturity (total number of days before the optimal picking date)
Model 1 2 3 4 5 6
Year 1997 1998 1997–1998 1997–1998a 1997–1998 1997–1998
Velm+Rillaar Velm+Rillaar
Velm+Rillaar Velm+Rillaar
Orchards Velm Velm+Rillaar included
+Lendelede
+Lendelede +Lendelede +Lendelede +Lendelede
780–2000c 380–2000 380–2000
Spectrum range 380–2000 380–2000 380–800b
(nm)
Number of samples
842 942
696 942
246
In calibration 942
696
246 942 100 942 942
In validation
0.94 0.90 0.85
Calibration 0.96 0.91 0.88
correlation
0.90 0.90 0.84
0.93 0.86
Validation 0.93
correlation
9 10
cPLS 9 9 8 9
components
4.55 5.64 6.86 7.01 8.81 7.77
SEC
SEP 5.81 6.06 7.40 7.31 9.05 8.53
aModel with test set.
Table 4
PLS-models for soluble solids content
2 3 4
1 5
Model 6 7
1997 1998
Year 1997 1998 1998 1997–1998 1997–1998a
Velm Velm+Rillaar Velm
Velm Rillaar
Orchards included Velm Velm
+Lendelede
Spectrum range 780–2000b 380–2000 380–2000 380–2000 380–2000 380–2000 380–2000 (nm)
Number of samples
244 678
In calibration 244 360 254 604 454
244 678
In validation 244 360 254 604 150
0.91 0.92 0.83 0.87 0.72
Calibration 0.88 0.85
correlation
0.89 0.89 0.80 0.84 0.73 0.84 0.84
Validation correlation
7 8 7
cPLS components 5 6 11 8
SEC 0.51 0.49 0.76 0.66 0.72 0.64 0.70
0.58 0.82 0.73
SEP 0.59 0.86 0.73 0.77
aModel with external validation set. bOnly the NIR segment of the spectrum.
apples was well predicted (validation correlation between 0.90 and 0.93, SEP between 5.81 and 7.40). Calibration models for 1 year (model 1 and 2) in particular were able to predict the number of days before harvest more accurately than the models constructed using data of 2 years (model 3, Fig. 3). However, the latter calibration models are more robust. If a model based on the VIS-part (model 5) is compared with one based on the NIR-part (model 6), a small difference is noticed in the predictive power of the models. So it seems that both of the regions possess information about the maturity of the apples and that not only the colour change is used to create the model. But the accuracy of the models increases when both regions are incorporated into one model (model 3) (SEP from 9.05 and 8.53 decreases to 7.40). To test whether external data can be predicted well, model 4 was built with 842 calibration samples and tested with an external validation set of 100 randomly chosen samples. The validation correla-tion did not decrease; the SEP increased a little. This means that the model maintained its predic-tion potential for new data.
3.3. Prediction of indi6idual quality characteristics
Calibration models were established for the
sol-uble solids content. The results are summarised in Table 4 and strong differences are noticed. Con-trary to the models of 1997 (model 1 and 2), better results were obtained for the models of 1998 when the visible part of the spectra was taken into account (model 3, data NIR-segment not shown). Also for the soluble solids content, differences were noticed between the different or-chards. The data of the orchard Velm were more accurately predicted (model 4, correlation, 0.84, SEP, 0.73) than the data of the orchard Rillaar (model 5, correlation, 0.73, SEP, 0.86). Model 3, which were based on the data of the different orchards, resulted in a validation correlation of 0.80 (SEP, 0.82), which is in between the correla-tions of the models of the separate orchards (models 4 and 5). When excluding the orchard Rillaar from the models, better predictions were obtained (model 6, correlation of 0.84). Model 7, which was established using an external validation set of 150 samples (Fig. 4) approached the same results as the models based on cross-validation. This means that the calibration model can be used to measure the soluble solids contents of precli-macteric apples of the orchard Velm by means of VIS/NIR reflectance spectroscopy.
(models 1 and 2) and the model of Rillaar (model 3) all had similar correlation values (0.82 – 0.86) and SEPs between 1.73 and 1.91. But when all the data were combined, the correlation values de-creased considerably (model 4). For this decrease, the influence of the year is minimal. This is derived from model 5, which is a model for 2
years of one orchard. The obtained validation correlation is similar to those for the individual years. It is therefore clear that the decrease in the correlation coefficient must originate from the differences between the orchards. As the individ-ual calibration models for the orchards Velm and Rillaar had high correlation values, it was
sus-Fig. 4. Calibration model for soluble solids content (combination of seven cultivars of the orchard Velm over a 2-year period) with an external validation set of randomly chosen 150 samples.
Table 5
PLS-models for acidity
6 7
2 3
Model 1 4 5
1997–1998 1997–1998
Year 1997 1998 1998 1998 1997–1998
Orchards Velm Velm Rillaar Velm+Rillaar Velm Velm+Rillaar Velm+Rillaar included
+Lendelede +Lendelede
Spectrum range 380–2000 380–2000 380–2000 380–2000 380–2000 380–2000 380–2000 (nm)
Number of samples
922
604 858
254
In calibration 244 360 678
922 858
360 254 678
In validation 244 604
0.77 0.80 0.78 0.82
0.88
Calibration 0.84 0.88
correlation
0.80
0.73 0.82 0.76
0.82
Validation 086 0.82
correlation
4 5 7 7 5 5
cPLS 7
components
2.13 1.99
1.78 1.57 2.14
SEC 1.64 1.90
2.26 2.07
1.90
Table 6
PLS-models for firmness
2 3 4 5
Model 1 6
1998 1998 1998
1997 1997–1998
Year 1997–1998
Velm Velm Rillaar Velm+Rillaar Velm+Rillaar Velm
+Lendelede +Lendelede
380–2000 380–2000 380–2000 380–2000 380–2000 380–2000 Spectrum range
(nm)
Number of samples
376
In calibration 225 256 696 921 601
376 256 696
255 921
In validation 601
Calibration 0.83 0.84 0.84 0.82 0.80 0.80
correlation
0.79 0.81 0.78
Validation 0.78 0.77 0.78
correlation
6 6
cPLS components 5 8 9 7
1.04 0.88 1.05
0.81 1.04
SEC 1.04
1.12 1.02
SEP 0.90 1.13 1.12 1.11
Fig. 5. Calibration model (combination of two cultivars, three orchards over a 2-year period) for firmness.
pected that the orchard Lendelede was responsible for the decrease in the correlation coefficient when all orchards were considered simultaneously. The latter orchard was therefore excluded from the model (model 7) and again a correlation of 0.80 and SEP of 2.07 were found. In future, juice of individual apples rather than of mixtures, will be
used to determine the acidity. The accuracy of the models will then increase most probably.
clear from the regression plot of model 5 (Fig. 5). The data cloud is flattened because of the maxi-mum possible firmness measurement of 12 kg. In future, another measurement device with a larger measurement range will be applied in order to obtain better results.
3.4. Conclusions
The results indicate that it is possible to mea-sure apple maturity for harvest of individual
culti-vars within an orchard using
VIS/NIR-spectroscopy. The number of days be-fore the optimum harvest date was well predicted (validation correlation between 0.90 and 0.93). Another way to predict optimal harvest data is with the Streif index. For this parameter, rela-tively high validation correlation values were ob-tained (0.85 – 0.90). Since internal and external quality attributes change with maturation, the number of days before harvest was correlated with many of the attributes. As a consequence, results for the latter were superior to those for the Streif index, which is based on only three maturity characteristics. Individual quality characteristics could also be predicted well with the spectral data. Validation correlation values of 0.80 – 0.90 were found for soluble solids and acidity. Predic-tion of soluble solids and Streif index appears to be orchard-dependent, and, as a consequence, a combined prediction model across orchards and cultivars was not sufficiently accurate.
Acknowledgements
The authors gratefully acknowledge funding by the Flemish Minister for Science and Technology, the research council of the KULeuven, and the project OT/99-22. Author Ann Peirs is a doctoral fellow of IWT.
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