A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy.

BACKGROUND: Visible-near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400-1100 nm. RESULTS: A total of 196 middle-early season and 219 late season apples (Malus domestica Borkh.) cvs 'Aroma' and 'Holsteiner Cox' samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets ('smooth fractionator', by date of measurement after harvest and random). Using the 'smooth fractionator' sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of 'Aroma' apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R(2) cal = 0.60/0.60; SEC = 0.88/0.88°Brix; Biascal = 0.00/0.00; R(2) val = 0.33/0.44; SEP = 1.14/1.03; Biasval = 0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar 'Holsteiner Cox' produced inferior results as compared with 'Aroma'. CONCLUSION: It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The 'smooth fractionator' protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible-NIR prediction models. The implication is that by using such 'efficient' sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R(2), RMSECV and RMSEP for 'Aroma' apples.

Nondestructive, stereological estimation of canopy surface area.

We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from a canopy using the smooth fractionator, (ii) sampling of leaves from the selected plants using the fractionator, and (iii) area estimation of the sampled leaves using point counting. We apply this procedure to estimate the total area of a chrysanthemum (Chrysanthemum morifolium L.) canopy and evaluate both the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting is high. Using a grid intensity of 1.76 cm(2)/point we estimated plant and canopy surface areas with accuracies similar to or better than those obtained using image analysis and a commercial leaf area meter. For canopy surface areas of approximately 1 m(2) (10 plants), the fractionator leaf approach with sampling fraction equal to 1/9 followed by point counting using a 4.3 cm(2)/point grid produced a coefficient of error of less than 7%. The smooth fractionator can be used to ensure that the additional contribution to the estimator variance due to between-plant variability is small.

Latent manganese deficiency increases transpiration in barley (Hordeum vulgare).

To investigate if latent manganese (Mn) deficiency leads to increased transpiration, barley plants were grown for 10 weeks in hydroponics with daily additions of Mn in the low nM range. The Mn-starved plants did not exhibit visual leaf symptoms of Mn deficiency, but Chl a fluorescence measurements revealed that the quantum yield efficiency of PSII (F(v)/F(m)) was reduced from 0.83 in Mn-sufficient control plants to below 0.5 in Mn-starved plants. Leaf Mn concentrations declined from 30 to 7 microg Mn g(-1) dry weight in control and Mn-starved plants, respectively. Mn-starved plants had up to four-fold higher transpiration than control plants. Stomatal closure and opening upon light/dark transitions took place at the same rate in both Mn treatments, but the nocturnal leaf conductance for water vapour was still twice as high in Mn-starved plants compared with the control. The observed increase in transpiration was substantiated by (13)C-isotope discrimination analysis and gravimetric measurement of the water consumption, showing significantly lower water use efficiency in Mn-starved plants. The extractable wax content of leaves of Mn-starved plants was approximately 40% lower than that in control plants, and it is concluded that the increased leaf conductance and higher transpirational water loss are correlated with a reduction in the epicuticular wax layer under Mn deficiency.