Mid-infrared spectroscopy for the rapid quantification of eucalyptus oil adulteration in Australian tea tree oil (Melaleuca alternifolia).

Essential oil distilled from Melaleuca alternifolialeaves, commonly known as tea tree oil, is well known for its biological activity, principally its antimicrobial properties. However, many samples are adulterated with other, cheaper essential oils such as eucalyptus oil. Current methods of detecting such adulteration are costly and time-consuming, making them unsuitable for rapid authentication screening. This study investigated the use of mid-infrared (MIR) spectroscopy for detecting and quantifying the level of eucalyptus oil adulteration in spiked samples of pure Australian tea tree oil. To confirm the authenticity of the tea tree oil samples, GC-MS analysis was used to profile 37 of the main volatile constituents present, demonstrating that the samples conformed to ISO specifications. Three chemometric regression techniques (PLSR, PCR and SVR) were trialled on the MIR spectra, along with a variety of pre-processing techniques. The best-performing full-wavelength PLSR model showed excellent prediction of eucalyptus oil content, with an R2CV of 0.999 and RMSECV of 1.08 % v/v. The RMSECV could be further improved to 0.82 % v/v through a moving window wavenumber optimisation process. The results suggest that MIR spectroscopy combined with PLSR can be used to predict eucalyptus oil adulteration in Australian tea tree oil samples with a high level of accuracy.

Microplastic adulteration in homogenized fish and seafood - a mid-infrared and machine learning proof of concept.

The objective of this study was to assess the ability of utilizing attenuated total reflection mid-infrared (ATR-MIR) spectroscopy in combination with machine learning techniques to classify the presence of different types of microplastics in artificially adulterated fish and seafood samples. Different polymers namely poly-vinyl chloride (PVC), polycarbonate (PC), polystyrene (PS), polypropylene (PP) and low (LDPE) and high-density polyethylene (HDPE) were mixed with homogenized fish and seafood samples. Homogenized samples were analyzed using MIR spectroscopy and classification models developed using machine learning algorithms such as partial least squares discriminant analysis (PLS-DA). The results of this study revealed that it was possible to identify between adulterated and non-adulterated samples as well as the different microplastic types added to the homogenized samples using ATR-MIR spectroscopy. This study confirmed the ability of combining machine learning methods with ATR-MIR spectroscopy to directly analyze microplastic adulteration in fleshy foods such as fish and seafood. This proof-of-concept study can be utilized and extended to monitor the presence of plastics either in a wide range of fleshy foods or along the entire food value chain.

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy.

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum (Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

Antioxidant capacity and vitamin E in barley: Effect of genotype and storage.

Antioxidants, including vitamin E, may have a positive effect on human health and prolong storage of food items. Vitamin E content and antioxidant capacity were measured in 25 barley genotypes before and after 4 months storage at 10 °C using high performance liquid chromatography (HPLC) and ability to scavenge DPPH radicals, respectively. As expected, α-tocotrienol (α-T3) and α-tocopherol (α-T) were the predominant tocol isomers. Vitamin E content and antioxidant capacity varied significantly among genotypes. Vitamin E ranged from 8.5 to 31.5 µg/g dry weight (DW) while ascorbic acid equivalent antioxidant capacity (AEAC) varied from 57.2 to 158.1 mg AEAC/100 g fresh weight (FW). Generally, lower vitamin E content or antioxidant capacity was observed in hulless or coloured genotypes. These results suggest that some genotypes are potential candidates for breeding of barley cultivars with high vitamin E content or antioxidant capacity at harvest, even after storage.

Identification and quantification of a marker compound for 'pepper' aroma and flavor in shiraz grape berries by combination of chemometrics and gas chromatography-mass spectrometry.

'Black pepper' aroma and flavor is important to some Australian Shiraz red wine styles but the aroma compounds involved have yet to be identified, and no objective analytical method to assess 'pepper' grape aromas is available to date. Samples of potentially 'spicy'/'peppery' grapes were obtained from vineyards in South Australia and Victoria over two vintages. The important sensory attributes of the grapes, including the aroma descriptor 'pepper', were rated by a sensory panel. The sensory study revealed a strong correlation between the intensity of 'pepper' aroma and the intensity of 'pepper' flavor perceived on the palate. The grape homogenates were analyzed by static headspace GC-MS using a cool inlet system. Vectors obtained by analysis of over 13 000 individual mass spectra per grape sample were then subjected to multivariate analyses. Both principal component analysis and partial least-squares regression were used to develop multivariate models based on mass spectra and aroma descriptors to explain the intensity of the rating of the 'pepper' character. Corresponding differences in mass spectra and aroma were observed among vineyards and from the same vineyards in different years. Additional optimization of the methodology enabled selection of a single region of the GC-MS chromatogram that allowed prediction of 'pepper' aroma intensity with a correlation coefficient >0.98 and led to the identification of alpha-ylangene, a tricyclic sesquiterpene. To assess the potential of alpha-ylangene as a marker for this sensory characteristic, a method for alpha-ylangene analysis of grapes and wine using HS-SPME-GC-MS was developed. Although not a significant aroma compound by itself, alpha-ylangene was a satisfactory marker for the 'pepper' aroma in grapes and wine, and its concentration showed similar discrimination between 'peppery' vineyards and vintages as that obtained using the multivariate models. Despite its presence in grapes, we could not detect alpha-ylangene in wine.