Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality.
Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum.
A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.
Superconducting Quantum Interference Devices (SQUIDs) have made the detection of low-field (LF) and ultra-low field nuclear magnetic resonance (ULF-NMR) a reality. The latter has been proven to be a potential tool for non-destructive quality testing of horticultural products, amongst many other applications. High-Temperature Superconductor (HTS) dc SQUIDS are likely to allow for the development of not only low-cost NMR systems but also prototypes that are mobile and easily maintainable. A HTS dc SQUID was manufactured on an YBCO thin film, using a novel laser based lithography method. The lithography was implemented by a new laser system developed in-house, as a model of low-cost lithography systems. The junctions of the dc SQUID were tested and displayed normal I-V characteristics in the acceptable range for the application. In order to determine the viability of low-field NMR for non-destructive quality measurement of horticultural products, a commercial HTS dc SQUID-NMR system was used to measure quality parameters of banana during ripening. The trend of color change and sugar increase of the banana during ripening were the most highly correlated attributes to the SQUID-NMR measured parameter, average T1 (spin-lattice relaxation time). Further studies were done, that involved processing of the NMR signal into relaxation time resolved spectra. A spectral signature of banana was obtained, where each peak is a T1 value corresponding to a proton pool, and is reported here. These results will potentially lead to deeper understanding of the quality of the samples under study.