Abstract
Vibrational spectroscopy was used in combination with chemometrics to understand the structure and properties of a series of different biological systems. The biological systems examined included artificially light aged, consolidated, and dyed harakeke fibres; ripe and storage breakdown disordered (SBD) kiwifruit; and polymer coated graphene treated fabric systems. These systems were studied in the bulk by Raman, infrared (IR), and near-infrared (NIR) spectroscopy in addition to confocal Raman microscopy for microscopic analysis. The introduction presents an overview of vibrational spectroscopy (Raman and IR), including dispersive, Fourier transform (FT) configurations, confocal Raman microscopy instrumentation, and chemometric data analysis methods.
Raman and IR spectroscopy was applied to understand the properties of paru dyed harakeke fibres and the interaction of a series of consolidants on these fibres. The consolidants were evaluated to determine if they acted in a protective manner upon the fibres when exposed to artificial light aging (similar to aging under conservation conditions in museums). Four types of paru dyed (hinau dye and manuka-kanuka dye mixed with two types of muds), and non-dyed harakeke fibres (Phormium tenax) were examined. Each dye type was studied with or without the presence of consolidants (n = 5, sodium alginate, zinc alginate, Paraloid B-72™, TRI-Funori™, and Methocel™A4C) with three levels of consolidant loading per consolidant. For bulk analysis, Raman and IR spectra were analysed via visual inspection, bi- and multi-variate analysis techniques. Exploration of the nature of the data include band intensity ratios and principal component analysis (PCA). Regression against parameters such as consolidant loading were carried out using partial least squares regression (PLSR). Classification of various groupings such as dye type or consolidant type were carried out using support vector machine (SVM), principal component analysis-linear discriminant analysis (PCA-LDA), and partial least squares-discriminant analysis (PLS-DA). The spectral data including PCA, revealed five main insights over the samples. The first one is that the spectra were consistent with paru dyed harakeke fibres had been dyed using iron tannate dyes. For PCA of IR spectral data from non-consolidated harakeke fibre samples, the samples were differentiated based on IR signatures of fibres (cellulose and other fibrous compounds) and the mud, while for Raman data, the separation was based on dye type. In PCA for IR data from the consolidated samples, IR signals associated with the consolidant dominated the variance in PC space. This partially masked the effects of artificial light aging. The classification techniques classified the samples based on dye type, mud type, dye category, aging stage, and consolidant type. The PLSR models for each consolidant have the potential of detecting the loading level of applied consolidant to the fibre. However, Raman spectroscopy was not effective in evaluating the potential protective effects of consolidation at light aging with bulk analysis. Raman mapping of fibre cross-sections was utilised to examine the distribution of the consolidant on the harakeke fibre. Univariate (band integrals) and multivariate (true component analysis: TCA) analysis of Raman mapping were used to detect, distinguish between and visualize the fibre versus consolidant signals. Both chemical imaging methods showed that the consolidant accumulated among the intercellular spaces of the fibre morphology and did not penetrate inside the cells.
It is desirable for producers to have a uniform and consistent kiwifruit product for export and domestic sales. Early detection of disorders such as SBD within the fruit before distribution is desirable. Raman spectroscopy was examined as a potential candidate technology to detect SBD along with understanding the spectral changes in the ripening process of normal kiwifruit. Spectral analyses were carried out on fruit harvested over two subsequent years (2018 and 2019). PCA was used to explore the inherent variance within the dataset and differentiated SBD from normal fruit. In order to detect SBD fruit among normal fruit, the 2018 fruit sample set was used to construct, validate, and test the SVM and PCA-LDA models. In addition, the subsequent year fruit sample set (2019) was used to assess the robustness of the constructed model considering test set accuracy, sensitivity, and specificity. SVM models were developed and resulted in a 93 % accuracy, 85 % sensitivity, and 100 % specificity to differentiate the test set fruit (2018 season). PCA-LDA models built with the same data set gave >83 % test accuracy, >75 % sensitivity and specificity. Models showed robustness with samples from 2019 season. The clustering of cold stored and ripe fruit in the PCA space is based on the chemical variance which was attributed to differences in carotenoid and sugar content.
An initial probing study was conducted on same graphene based dyes treated cotton and wool fibres. Raman microscopic maps were collected to figure out the distribution of reduced graphene oxide (rGO) or graphene ink + silicone-based coating polymer (polydimethylsiloxane, Granger's® clothing repel, and SYLGARD™184) applied to cotton and wool fabrics at a microscopic scale. Both uni- and multi-variate (TCA) methods for data analysis demonstrated uneven deposition of rGO on the surface of the fibres and accumulation on the edges of fibre cells (via cross-section). For the polymer coated graphene ink treated fabrics, univariate analysis was not suitable due to the absence of unique spectral features for each component within the sample. TCA generated component spectra consistent with the spectral signatures of dried ink, wet ink and fibres. The polymer coating SYLGARD™184 applied to cotton fabric was the only polymer detected of the fabric-ink-polymer coating combinations studied.
These studies have demonstrated that vibrational spectroscopy can be applied, combined with both uni- and multi-variate analytical methods, to understand and characterize the structure and properties of biological systems using various biological samples, and some limitations of vibrational spectroscopic techniques and data analytical methods while studying such biological materials. This work can impact on science community as well as fields of culture and conservation, horticulture and food industry, and smart textiles industry.