Truffles represent the best known and most expensive edible mushroom. Known as Ascomycetes, they belong to the genus Tuber and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of up to 3000–5000 euros per kilogram (Tuber magnatum PICO). Amongst black truffles, the species Tuber melanosporum VITTAD. is highly regarded for its organoleptic properties. Nonetheless, numerous different sorts of black truffle are offered at lower prices, including Tuber aestivum VITTAD., Tuber indicum and Tuber uncinatum, which represent the most frequently consumed types. Because truffles do not differ visually for inexperienced consumers, food fraud is likely to occur. In particular, for the highly prized Tuber melanosporum, which morphologically forms very similar fruiting bodies to those of Tuber indicum, there is a risk of fraud via imported truffles from Asia. In this study, 126 truffle samples belonging to the four mentioned species were investigated by four different NIR instruments, including three miniaturized devices—the Tellspec Enterprise Sensor, the VIAVI solutions MicroNIR 1700 and the Consumer Physics SCiO—working on different technical principles. Three different types of measurement techniques were applied for all instruments (outer shell, rotational device and fruiting body) in order to identify the best results for classification and quality assurance in a non-destructive manner. Results provided differentiation with an accuracy up to 100% for the expensive Tuber melanosporum from Tuber indicum. Classification between Tuber melanosporum, Tuber indicum, Tuber aestivum and Tuber uncinatum could also be achieved with success of 100%. In addition, quality monitoring including discrimination between fresh and frozen/thawed, and prediction of the approximate date of harvesting, was performed. Furthermore, feasibility studies according to the geographical origin of the truffle were attempted. The presented work compares the performance for prediction and quality monitoring of portable vs. benchtop NIR devices and applied measurement techniques in order to be able to present a suitable, accurate, fast, non-destructive and reliable method for consumers.

The aim of the presented study was to qualitatively and quantitatively determine the chemical composition of oak bark extracts in order to gain insights into the effectiveness as alternative medication for various diseases. The primary emphasis was on developing a near-infrared spectroscopy (NIRS) method for precise quantification of two key polyphenolic compounds, specifically gallic acid and catechin, in form of a fast and non-destructive quality control. A comprehensive dataset consisting of 48 samples from various production batches was analyzed throughout this research. Qualitative analysis was conducted using High Performance Liquid Chromatography coupled with a mass detector (LC-MS) to separate and identify individual components of the oak bark extract. Individual components were identified, confirmed and quantified using existing literature combined with appropriate standard references. Whereas the predominant nature of identified substances was of polyphenolic nature. Furthermore, a semi-quantitative assessment was additionally performed for eight identified constituents to identify their chemical stability or possible occurring transformations during storage, utilizing quantification via internal standard met in order to identify fluctuations and chemical variability within oakbark, five key components were precisely quantified using LC-MS and corresponding standard substances. For this purpose, HPLC measurements coupled to an Ultraviolet/Visible (UV/Vis) detector were utilized as reference method. NIRS measurements were performed on a FT-NIR benchtop device in transmission mode. Partial least squares regression (PLSR) was then applied for model building, after identifying the optimal spectral pretreatment. Model evaluation was performed using leave-one-out-cross validation followed by evaluation of an independent test set. The model proved promising results for the quantification of gallic acid on the benchtop device with a standard error of cross validation (SECV) of 13.41 mg/L and a standard error of prediction (SEP) of 19.33 mg/L, while the absolute concentrations of the different batches analyzed ranged from 126.49 to 332.54 mg/L. For the quantification of catechin the SECV was reported at 23.61 mg/L, the SEP at 32.35 mg/L with sample concentrations falling between 13.50 and 383.72 mg/L. In this study, we introduce various analytical methodologies for both qualitative and quantitative assessment of a complex phytochemical sample, specifically oak bark extract, aimed at identifying and confirming the presence of active compounds within the extract.

This work is the first to describe the use of Direct Analysis in Real Time mass spectrometry (DART-MS) for the rapid and cost-effective quality control of cigars. Multivariate analyses (PCA and LDA) on mass spectra led to highly efficient models for the discrimination of tobacco products. In addition to the geographic origin of the cigars, different pretreatments of the tobacco leaves could also be determined. Cross-validations of the models yielded prediction accuracies up to 92.18%. All results were compared using LC-MS as reference method. Multivariate analyses of chromatograms also yielded powerful models with correctness scores up to 88.1%. However, the models generated with LC-MS were neither able to determine the geographic origin of the tobacco leaves nor different pretreatments. In addition to qualitative analysis, the nicotine content of tobacco leaves was also determined via both methods. While precise quantification was not possible with DART-MS, a correlation coefficient of >0.96 was achieved in direct comparison with LC-MS, which allows semi-quantitative statements about the nicotine content without any problems.

Bone analyses using mid-infrared spectroscopy are gaining popularity, especially with handheld spectrometers that enable on-site testing as long as the data quality meets standards. In order to diagnose Staphylococcus epidermidis in human bone grafts, this study was carried out to compare the effectiveness of the Agilent 4300 Handheld Fourier-transform infrared with the Perkin Elmer Spectrum 100 attenuated-total-reflectance infrared spectroscopy benchtop instrument. The study analyzed 40 non-infected and 10 infected human bone samples with Staphylococcus epidermidis, collecting reflectance data between 650 cm⁻¹ and 4000 cm⁻¹, with a spectral resolution of 2 cm⁻¹ (Agilent 4300 Handheld) and 0.5 cm⁻¹ (Perkin Elmer Spectrum 100). The acquired spectral information was used for spectral and unsupervised classification, such as a principal component analysis. Both methods yielded significant results when using the recommended settings and data analysis strategies, detecting a loss in bone quality due to the infection. MIR spectroscopy provides a valuable diagnostic tool when there is a tissue shortage and time is of the essence. However, it is essential to conduct further research with larger sample sizes to verify its pros and cons thoroughly.

Osteomyelitis is a bone disease caused by bacteria that can damage bone. Raman handheld spectroscopy has emerged as a promising diagnostic tool for detecting bone infection and can be used intraoperatively during surgical procedures. This study involved 120 bone samples from 40 patients, with 80 samples infected with either Staphylococcus aureus or Staphylococcus epidermidis. Raman handheld spectroscopy demonstrated successful differentiation between healthy and infected bone samples and between the two types of bacterial pathogens. Raman handheld spectroscopy appears to be a promising diagnostic tool in bone infection and holds the potential to overcome many of the shortcomings of traditional diagnostic procedures. Further research, however, is required to confirm its diagnostic capabilities and consider other factors, such as the limit of pathogen detection and optimal calibration standards.