Yellow turmeric (Curcuma longa) is widely used for culinary and medicinal purposes, and as a dietary supplement. Due to the commercial popularity of C. longa, economic adulteration and contamination with botanical additives and chemical substances has increased. This study used FT-IR spectroscopy for identifying and estimating white turmeric (Curcuma zedoaria), and Sudan Red G dye mixed with yellow turmeric powder. Fifty replicates of yellow turmeric—Sudan Red mixed samples (1%, 5%, 10%, 15%, 20%, 25% Sudan Red, w/w) and fifty replicates of yellow turmeric—white turmeric mixed samples (10%, 20%, 30%, 40%, 50% white turmeric, w/w) were prepared. The IR spectra of the pure compounds and mixtures were analyzed. The 748 cm⁻¹ Sudan Red peak and the 1078 cm⁻¹ white turmeric peak were used as spectral fingerprints. A partial least square regression (PLSR) model was developed for each mixture type to estimate adulteration concentrations. The coefficient of determination (R²_v) for the Sudan Red mixture model was 0.97 with a root mean square error of prediction (RMSEP) equal to 1.3%. R²_v and RMSEP for the white turmeric model were 0.95 and 3.0%, respectively. Our results indicate that the method developed in this study can be used to identify and quantify yellow turmeric powder adulteration.

Structural health and construction security are important problems in civil engineering. Regular infrastructure inspection and monitoring methods are mostly performed manually. Early automatic structural health monitoring techniques were mostly based on contact sensors, which usually are difficult to maintain in complex infrastructure environments. Therefore, non-contact infrastructure inspection and monitoring techniques received increasing interest in recent years, and they are widely used in all aspects of infrastructure life, owing to their convenience and non-destructive properties. This paper provides an overview of vision-based inspection and vision–laser-based monitoring techniques and applications. The inspection part includes image-processing algorithms, object detection, and semantic segmentation. In particular, infrastructure monitoring involves not only visual technologies but also different fusion methods of vision and lasers. Furthermore, the most important challenges for future automatic non-contact inspections and monitoring are discussed and the paper correspondingly concludes with state-of-the-art algorithms and applications to resolve these challenges.

The Haugh unit (HU) is the most widely used measure to assess egg freshness. It is crucial and difficult to measure albumen height as a variable of the HU, especially for stale eggs. Albumen pH is strongly correlated to the HU and rarely affected by age or strain, except for storage. The prediction of albumen pH is a better indicator of egg freshness than the HU. Albumen is separated from the yolk in the albumen pH test, while in the whole egg pH measurement, it is not necessary to separate the albumen from the yolk. The objective of this research was to compare the albumen pH with the whole egg pH using visible near-infrared spectroscopy. Spectra were acquired using transmitted light on white-shelled eggs. Pretreatment methods containing multiplicative scatter correction, the standard normal variate (SNV), the first derivative, and the second derivative were used. A partial least square regression (PLSR) was used to set up prediction models. The results illustrated that PLSR models preprocessed by the SNV of albumen pH and whole egg pH were superior to other models. In the best prediction models, the correlation coefficients for albumen pH and whole egg pH were 0.923 and 0.752, and the root mean square error values were 0.170 and 0.265, respectively. The prediction models of albumen pH were better than those of whole egg pH using the PLSR method. In addition, the relevant information concerning albumen pH and whole egg pH ranged from 550 to 850 nm.