Chromium­(III) sulfate is extensively used in leather processing to stabilize the collagen molecules in hides and skins. Although its excess usage causes severe environmental pollution and health concerns, the role of chromium in stabilizing collagen still remains poorly understood. For the first time, by integrating a number of techniques, including real-time small-angle X-ray scattering, differential scanning calorimetry and natural cross-link analysis, we reveal crucial molecular-level indicators of collagen stability. The results indicate that collagen molecules achieve maximum molecular stability at concentrations as low as 1.8 wt % even if excess chromium (>3.7%) is introduced into the collagen matrix. At low concentrations (1.8% to 3.7%), the active amino acid residues are saturated via covalent bonding with chromium. Any excess chromium interacts purely non-covalently with the collagen molecule and, we propose, can be substituted by environment-friendly alternatives. Further, important natural cross-links, which are crucial in imparting mechanical strength, were observed to decrease with increasing chromium concentration, highlighting the adverse impact of chromium­(III) sulfate on collagen matrix and the importance of identifying alternative cross-linking agents. Our findings provide tools which will enable the evaluation of greener tanning agents to facilitate a more sustainable future for the leather industry.

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SAXS has been applied to structural determination in leather. The SAXS beamline at the Australian Synchrotron provides 6 orders of magnitude dynamic range, enabling a rich source of structural information from scattering patterns of leather sections. SAXS patterns were recorded for q from 0.004 to 0.223 A(-1). Collagen d spacing varied across ovine leather sections from 63.8 nm in parts of the corium up to 64.6 nm in parts of the grain. The intensity of the collagen peak at q = 0.06 A(-1) varied by 1 order of magnitude across ovine leather sections with the high-intensity region in the corium and the low intensity in the grain. The degree of fiber orientation and the dispersion of the orientation has been quantified in leather. It is shown how the technique provides a wealth of useful information that may be used to characterize and compare leathers, skin, and connective tissue.

The effect of sodium silicates on collagen structure during leather processing was investigated. Small angle X-ray scattering (SAXS) and differential scanning calorimetry (DSC) reveal that the molecular structure and thermal stabilities of the sodium silicate treated leathers (So-Si and So-Si + BCS) were different to the conventionally processed chromium treated leathers (BCS). The collagen fibrils were observed to be coated by aggregates of silica, which did not affect the axial periodicity (D-period) of the collagen molecules. However, an increase in collagen fibril diameter was observed during the main tanning step when sodium silicates were used. This could be due to the swelling of collagen fibers from the high alkaline conditions of sodium silicates. From DSC studies, it was also found that sodium silicate treated samples impart no effect on collagen stabilization in the absence of chromium(III). However, a pseudostabilization effect is observed in the So-Si + BCS samples, possibly due to the inability of the collagen molecules to undergo conformational changes due to the silica coating on the collagen fibrils.The tanning of leather involves chemically intense processes leading to environmental pollution, resulting in a demand for cleaner but effective collagen stabilization mechanisms for the leather industry.1,2 Basic chromium(III) sulphate is the most common mineral tanning agent and, is preferred industrially because of the high hydrothermal stability. It has excellent properties in addition to relative short times required to produce nished leathers.3 However, poor uptake of chromium salts leads to high chemical and biological oxygen demand, and toxicity concerns relating to hexavalent chromium(VI) exposure, have led researchers to seek more environmentally friendly alternatives. 4-6Mineral tanning agents such as chromium, zirconium, aluminium, titanium and iron can all stabilize collagen and impart varying degrees of hydrothermal stability.3,7 Synthetic tanning agents (syntans) and vegetable tanning agents from plant polyphenols along with aldehydic cross-linkers are also commonly used, but typically in conjunction with mineral tannages.8 Combination tannages however, with or without mineral tanning agents, can overcome the issues that single tanning systems have with hydrothermal stability.9,10 For example, Vitolo and co-workers observed an increase in chromium uptake when sodium silicate was used in combination with basic chromium sulphate.11 Whilst pre-tans can improve the penetration and even distribution of the main tanning agents in the collagen matrix, a co-stabilizing agent can increase the efficiency of chrome uptake by reducing the amount of chrome required.8 With this idea of combining a weak and strong tanning agent, further studies have been carried out by investigating combinations of a number of tanning options and their effect on collagen stabilization. 4,6,9,12 Soluble silicates such as sodium silicate belong to a group of compounds that contain varying compositions of an alkali met...

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The collagen structure in skins is significantly influenced by the cross-linking chemistry adopted during leather processing. We have developed an in situ technique to measure real-time collagen structure changes using synchrotron-based small-angle X-ray scattering (SAXS). Three common mineral tanning systems, basic chromium sulfate (BCS), zirconium sulfate (ZIR) and an aluminosilicate-based reagent (ALS) were used to stabilize collagen in ovine skin. Studying the molecular changes by in situ SAXS revealed a range of tanning mechanisms: a complex combination of covalent cross-linking, electrostatic interactions and hydrogen bonding by BCS, hydrogen bonding interactions by ZIR, and the formation of colloidal aggregates by ALS. These results unravel the mechanisms of producing leathers with different properties, explaining why ZIR produces denser leathers while ALS produces softer leathers compared to conventional BCS leathers. ZIR and ALS are environment-friendly alternatives to BCS, and understanding their mechanisms is important for a more sustainable future for the leather industry.

There is a substantial loss of value in bovine leather every year due to a leather quality defect known as "looseness". Data show that 7% of domestic hide production is affected to some degree, with a loss of $35 m in export returns. This investigation is devoted to gaining a better understanding of tight and loose wet blue leather based on vibrational spectroscopy observations of its structural variations caused by physical and chemical changes that also affect the tensile and tear strength. Several regions from the wet blue leather were selected for analysis. Samples of wet blue bovine leather were collected and studied in the sliced form using Raman spectroscopy (using 532 nm excitation laser) and Attenuated Total Reflectance-Fourier Transform InfraRed (ATR-FTIR) spectroscopy. The purpose of this study was to use ATR-FTIR and Raman spectra to classify distal axilla (DA) and official sampling position (OSP) leather samples and then employ univariate or multivariate analysis or both. For univariate analysis, the 1448 cm − 1 (CH 2 deformation) band and the 1669 cm − 1 (Amide I) band were used for evaluating the lipid-to-protein ratio from OSP and DA Raman and IR spectra as indicators of leather quality. Curve-fitting by the sums-of-Gaussians method was used to calculate the peak area ratios of 1448 and 1669 cm − 1 band. The ratio values obtained for DA and OSP are 0.57 ± 0.099, 0.73 ± 0.063 for Raman and 0.40 ± 0.06 and 0.50 ± 0.09 for ATR-FTIR. The results provide significant insight into how these regions can be classified. Further, to identify the spectral changes in the secondary structures of collagen, the Amide I region (1600-1700 cm − 1) was investigated and curve-fitted-area ratios were calculated. The 1648:1681 cm − 1 (non-reducing: reducing collagen types) band area ratios were used for Raman and 1632:1650 cm − 1 (triple helix: α-like helix collagen) for IR. The ratios show a significant difference between the two classes. To support this qualitative analysis, logistic regression was performed on the univariate data to classify the samples quantitatively into one of the two groups. Accuracy for Raman data was 90% and for ATR-FTIR data 100%. Both Raman and ATR-FTIR complemented each other very well in differentiating the two groups. As a comparison, and to reconfirm the classification, multivariate analysis was performed using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The results obtained indicate good classification between the two leather groups based on protein and lipid content. Principal component score 2 (PC2) distinguishes OSP and DA by symmetrically grouping samples at positive and negative extremes. The study demonstrates an excellent model for wider research on vibrational spectroscopy for early and rapid diagnosis of leather quality.

Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.