Özdemir İbrahim Sani, Bureau Sylvie, Öztürk Bülent, Seyhan Ferda, Aksoy Hatice
TUBITAK Marmara Research Center, Food Institute, P.O. Box 21, 41470 Gebze, Kocaeli Turkey.
UMR-A-408, SQPOV INRA, Domaine St Paul, Site Agroparc, 84914 Montfavet, Avignon Cedex 9, France.
J Food Sci Technol. 2019 Jan;56(1):330-339. doi: 10.1007/s13197-018-3493-3. Epub 2018 Dec 10.
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, "Hacıhaliloğlu" and "Kabaaşı" 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.
针对土耳其两个具有重要商业价值的杏品种“Hacıhaliloğlu”和“Kabaaşı”，开发了傅里叶变换近红外（FT-NIR）模型，用于无损预测其可溶性固形物含量（SSC）、可滴定酸度（TA）、硬度和重量。构建的用于SSC预测的模型取得了良好的结果。我们还建立了一个可用于粗略估计杏重量的模型。然而，利用FT-NIR光谱法无法准确预测杏的TA和硬度。为了进行SSC预测，该研究又持续了3年。单品种和多品种模型的验证表明，模型性能在不同收获季节可能会有显著差异。当使用两到三个季节的数据时，模型的稳健性得到了提高。得出的结论是，为了开发可靠的杏SSC预测模型，应在多个收获季节收集光谱数据。
