Modern analytical methods based on non-destructive Near-Infrared (NIR) spectroscopy were successfully developed aiming at the following targets: the quantification of the antioxidant potential (polyphenolic compounds), the total sugar and total acid content of apples, the determination of the geographical origin of apples and the identification of adulterations of processed veal products with pork. On this basis, parallel development of several methods suited for laboratory, industrial or on-site application was realized by the use of different specialized instrumental setups. Advantages and drawbacks were considered in a detailed comparison of these instruments and methods. This includes the assessment of a novel measurement technique developed as part of this thesis to improve NIR based analyses of apples. Therefore, a specialized prototype was constructed to rotate samples while recording spectra (Surface Scanning). Results contributed significantly to the success of the EU funded cross-border project “OriginAlp”.
Furthermore, the outcomes of the project “Schnelltest”, funded by the Tyrolian government, are presented. In this respect, a quick-test was developed which enables untrained users to perform on-site analyses determining total sugar content and antioxidant potential of apples simultaneously by the use of a miniaturized handheld spectrometer. Results are presented in re-printed form of the original journal articles. The strategy for the development of novel NIR based analytical methods was elaborated and published as a chapter of a specialist book.
The most important results are summarized in brief hereinafter: Compared to the standard measurement procedure (point measurements), automated non-destructive Surface Scanning led to a lowering of the prediction errors for the determination of the soluble solids content (SSC) for Pink Lady (PL) apples with partial least squares (PLS) regression by 2.2 % and for Golden Delicious (GD) samples by 26 %. Prediction accuracy of the total acid of the fruits was raised by 3.5 % for GD apples. Prediction accuracy of polyphenol content was increased by 8.3 % and 15 % for PL and GD apples, respectively. Successful multivariate clustering was realized performing principal component analyses (PCA), to identify 160 GD apples from the alpine area (South Tyrol, Italy) against 235 GD samples cultivated in 20 countries (Belgium, Canada, Chile, China, Czech Republic, England, France, Germany, India, Italy, Japan, Moldowa, Morocco, Poland, Russia, Serbia, Slovenia, South Africa, Spain, Switzerland) using the spectroscopic data set derived from Surface Scanning.
Antioxidant capacity and SSC of seven cultivars of apples (Braeburn, Evelina, Gala, GD, Granny Smith, Jonagold, PL) were successfully determined applying a FT-NIR handheld spectrometer. On-site prediction of SSC of 92 apples was possible applying PLS analyses with a standard error of prediction (SEP) of 0.55 Bx (RPD=2.5, R2=0.76). Polyphenolic compounds of the peels were predicted with an SEP=0.13 % gallic acid equivalents of peels dry matter (RPD=2.8, R2=0.85). Applying Surface Scanning, SEP values decreased significantly by 39 % and 10 % compared to on-site analyses for the prediction of SSC and polyphenolic compounds, respectively.
Methods for laboratory use, industrial purposes and on-site analyses were developed to reveal intentional adulteration or accidental contamination of a pure veal product with pork and pork fat. Meat and fat adulteration could be detected down to the lowest level of contamination (10 %) applying the laboratory setup and the industrial fibre optics setup, regarding measurements through quartz and polymer packaging. Analyses with the on-site setup led to successful separation down to the lowest degree of contamination (10 %, measurement through quartz cuvettes) regarding meat adulteration and up to 20 % and 40 % contamination regarding the fat adulteration, performing measurements through quartz cuvettes and through polymer packaging, respectively.