Effect of cultivar and season on the robustness of PLS models for soluble solid content prediction in apricots using FT-NIRS.

FT-NIR models were developed for the non-destructive prediction of soluble solid content (SSC), titratable acidity (TA), firmness and weight of two commercially important apricot cultivars, "Hacihaliloglu" and "Kabaasi" from Turkey. The models constructed for SSC prediction gave good results. We could also establish a model which can be used for rough estimation of the apricot weight. However, it could not be possible to predict accurately TA and firmness of the apricots with FT-NIR spectroscopy. The study was further extended over 3 years for the SSC prediction. Validation of the both mono and multi-cultivar models showed that model performances may exhibit important variations across different harvest seasons. The robustness of the models was improved when the data of two or three seasons were used. It was concluded that in order to developed reliable SSC prediction models for apricots the spectral data should be collected over several harvest seasons.

Effect of fruit maturity level on quality, sensory properties and volatile composition of two common apricot (Prunus armeniaca L.) varieties.

The effect of maturity level on fruit quality properties, volatile composition and sensory attributes was investigated in two important apricot varieties (Hacihaliloglu and Kabaasi). The soluble solid content was used as the maturity index for the classification of apricots according to their maturity levels as immature (14-20 °Brix), mature (20-24 °Brix) and over-mature (> 24°Brix). Changes in the volatile composition of samples at different maturity levels were characterized using headspace solid phase micro-extraction gas chromatography-mass spectrometry. The results showed that the quality attributes of the Kabaasi were affected to a lesser extent by the maturity level than Hacihaliloglu. From the immature to over-mature, fruit weight, dry matter and pH increased while firmness and titratable acidity decreased (p < 0.05) in both varieties. Volatile composition was affected by both apricot variety and maturation levels. The main volatiles were aldehydes, alcohols, ketones, esters, terpenes and hydrocarbons. Compared to Kabaasi, the concentrations of the volatile compounds were higher in Hacihaliloglu regardless of the maturity levels. Among the samples, Hacihaliloglu at over-mature level received the highest "overall liking" score. The principal component analysis made on the measured quality attributes allowed the discrimination of apricot varieties and their maturity levels.

Factors influencing fungal and aflatoxin levels in Turkish hazelnuts (Corylus avellana L.) during growth, harvest, drying and storage: a 3-year study.

The levels aflatoxins in Turkish hazelnuts have been monitored over a 3-years period (2002-2004). Periodical sampling was made in 72 different orchards at different locations representative of the hazelnut-growing areas and post-harvest applications. Various parameters (aflatoxins, water activity, moulds) were analysed and environmental conditions (temperature and relative humidity) recorded during growing and at different stages of harvest and post-harvest processing, involving three different harvesting methods (collection in nets, from the ground, etc.) and four drying techniques (traditional sun-drying, mechanical drying, etc.). Fungal and aflatoxin analyses (HPLC) showed no significant difference except between samples which had been in contact with the ground and those which had not (at 95% confidence level). Aflatoxins levels from the orchard recorded a maximum of 0.77+/-0.08 ng g(-1) from a total of 1624 samples. Regarding harvesting and post-harvest processes, the only application where aflatoxins were detected was in samples which had been in direct contact with the ground (max. 3.18+/-0.03 ng g(-1)). Aflatoxin formation was low during storage (max. 0.34+/-0.003 ng g(-1)). As a result of mycological studies, a total of 5546 Aspergillus flavus (89%) and A. parasiticus (11%) species were isolated and identified from samples. The results indicated that harvesting hazelnuts into a canvas by shaking the trees, manual harvesting of mature hazelnuts where possible, use of jute instead of nylon sacks and mechanical drying technique would minimize aflatoxin levels in hazelnuts. These recommendations have been implemented and about 4000 people in the hazelnut industry have been trained in these practices.

Sampling hazelnuts for aflatoxin: effect of sample size and accept/reject limit on reducing the risk of misclassifying lots.

About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin sampling plans can be predicted, they can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. A method was developed to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16 aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers' risk) and accepting bad lots (buyers' risk) was demonstrated for various sampling plan designs.

Sampling hazelnuts for aflatoxin: uncertainty associated with sampling, sample preparation, and analysis.

The variability associated with the aflatoxin test procedure used to estimate aflatoxin levels in bulk shipments of hazelnuts was investigated. Sixteen 10 kg samples of shelled hazelnuts were taken from each of 20 lots that were suspected of aflatoxin contamination. The total variance associated with testing shelled hazelnuts was estimated and partitioned into sampling, sample preparation, and analytical variance components. Each variance component increased as aflatoxin concentration (either B1 or total) increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. The sampling, sample preparation, and analytical variances associated with estimating aflatoxin in a hazelnut lot at a total aflatoxin level of 10 ng/g and using a 10 kg sample, a 50 g subsample, dry comminution with a Robot Coupe mill, and a high-performance liquid chromatographic analytical method are 174.40, 0.74, and 0.27, respectively. The sampling, sample preparation, and analytical steps of the aflatoxin test procedure accounted for 99.4, 0.4, and 0.2% of the total variability, respectively.