Identification of adulteration in ground roasted coffees using UV–Vis spectroscopy and SPA-LDA

Multiplatform Path-ComDim study of Capixaba, indigenous and non-indigenous Amazonian Canephora coffees

Integrating diverse measurement platforms can yield profound insights. This study examined Brazilian Canephora coffees from Rondônia (Western Amazon) and Espírito Santo (southeast), hypothesizing that geographical and climatic differences along botanical varieties significantly impact coffee characteristics. To test this, capixaba, indigenous, and non-indigenous Amazonian canephora coffees were analyzed using nine distinct platforms, including both spectroscopic techniques and sensory evaluations, to obtain results that are more informative and complementary than conventional single-method analyses. By applying multi-block Path-ComDim analysis to the multiple data sets, we uncovered crucial correlations between instrumental and sensory measurements. This integrated approach not only confirmed the hypothesis but also demonstrated that combining multiple data sets provides a more nuanced understanding of coffee profiles than traditional single-method analyses. The results underscore the value of multiplatform approaches in enhancing coffee quality evaluation, offering a more detailed and comprehensive view of coffee characteristics that can drive future research and improve industry standards.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

Traditional Turkish Coffee with Medicinal Effect

Traditional Turkish coffee (TTC) is highly associated with caffeine and is known as a mind and heart stimulant as it helps keep tiredness at bay. Daily consumption of TTC naturally benefits human health such as anti-cancer, anti-diabetic, improved energy, anti-depression, reduced risk of heart disease, etc. The TTC was derived from particular types of Arabic coffee beans (ACB), and the preparation method of TTC is unique from other types of coffee. The main objective of the study was to investigate the therapeutic and biological effects of TTC. The ACB powder was characterized physicochemically using UV-Vis spectroscopy, Fourier transforms infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). In vitro analysis using HaCaT (Human keratinocyte cell line) proved the biocompatibility of ACB powder. Case studies which were focusing on healthy individuals as the research populace were conducted using TTC. Consumption of TTC was found beneficially compared to other types of coffee. The TTC was obtained from ACB, which was characterized by spectroscopic techniques and displayed biocompatibility due to the results on HaCaT cell lines. The TTC has beneficial therapeutic effects on individuals. According to statistical analysis, the disease-affected ratio of diabetes, heart disease, and depression was significantly decreased.

Three centuries on the science of coffee authenticity control

Food authenticity relies on genuineness and reliability according to the information displayed on the package. Since the 18th century, when coffee became popularized in the West, adulteration began. Several methods have been developed to detect different kinds of frauds and they have evolved as demands increased and new technologies were introduced. The evolution of the science of coffee authenticity control in the past three centuries is reviewed, focusing on the discrimination between coffee and other foods or between coffee and its by-products. The earliest chemical and physical methods are presented followed by methods developed in the 20th and 21st centuries using microscopy, chromatography and spectroscopy associated with advanced statistical tools, and DNA-based methods. In addition to non-food material, before the 20th century, chicory was the most studied food-adulterant. From the 20th century on, corn, coffee by-products, and barley were the most studied, followed by chicory, rice and other food items. Matrix effects seem to be among the most challenging problems in these approaches, associated with variations in roast degree, particle size (particularly in spectroscopy-based methods), and lack of control over reference samples regarding species and purity. Limits of detection vary considerably within each category, with most limits being too high for commercial use. DNA-based methods appear to be promising to assess coffee authenticity, given that the limits of detection and quantification are low, and specificity is higher than in other methods. Nevertheless, as roast intensity increases, the sensitivity of the method decreases. So far, most reported methods have not been validated and only a few have been tested on commercial brands, except for those involving microscopy which has been highly used for monitoring coffee authenticity although not always efficiently enough.

Honey authentication in terms of its adulteration with sugar syrups using UV-Vis spectroscopy and one-class classifiers

This work proposes the use of UV-Vis spectroscopy and one-class classifiers to authenticate honey in terms of their individual and simultaneous adulterations with corn syrup, agave syrup, and sugarcane molasses. Then, spectra of aqueous authentic (n = 73) and adulterated (n = 162) honey samples were recorded. Before the construction of OC-PLS and DD-SIMCA models, different pre-processing techniques were used to removed baseline shifts. The best result obtained by DD-SIMCA using offset correction, correctly classifying all the samples in the test set. Therefore, the proposed methodology can be used as a promising tool to authenticate honey and prevent fraudulent labeling, affording security to consumers and providing an alternative to regulatory agencies. Moreover, it avoids laborious sample preparation and additional operational costs, since the analytical information is acquired using a routine instrumental technique, without the need for any sample preparation step, other than dilution of the samples in water alone.

Non-destructive authentication of Gourmet ground roasted coffees using NIR spectroscopy and digital images

The growing demand for excellent-quality coffees allied with their symbolic aestheticization that add value to the products favor the adulteration practices and consequently economic losses. So, this work proposes the suitability of NIR spectroscopy and Digital Images (from CACHAS) coupled with one-class classification methods for the non-destructive authentication of Gourmet ground roasted coffees. For this, Gourmet coffees (n = 44) were discriminated from Traditional (n = 36) and Superior (n = 10) by directly analyzing their powder without any sample preparation. Then, OC-PLS and dd-SIMCA were used to construct the models. dd-SIMCA using offset correction for NIR and RGB histogram for CACHAS achieved the best results, correctly recognizing all the 90 samples in both the training and test sets. Therefore, the proposed methodologies can be useful for both the consumers and regulatory agencies because it confirms the elevated standards of excellence of Brazilian specialty coffees, preventing fraudulent labeling, besides following the Principles of Green Analytical Chemistry.

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near-infrared spectroscopy and multivariate analysis to identify affected chicken breasts

White Striping (WS) and Wooden Breast (WB) are emerging poultry myopathies that occur worldwide, affecting the quality of meat. The aim of this study was to evaluate the occurrence of N, WS, WB, and WS/WB (myopathies combined) in chicken breast from Brazilian commercial plant, comparing (1) inspection based on visual aspect and palpation of Pectoralis major muscle, and (2) identification of these myopathies by near-infrared Spectroscopy (NIRS). Chickens slaughtered at Brazilian commercial plant at four age ranges (4 to 5, 6 to 7, 8 to 9, and 65 weeks) were inspected. Spectral information was acquired using a portable NIR spectrometer, and classification models were performed using and Successive Projection Algorithm-Linear Discriminant Analysis (SPA-LDA) and Soft Independent Modeling of Class Analogy (SIMCA) to distinguish normal and affected muscles. Results showed that occurrence of myopathies was aggravated by age of slaughter, as chicken slaughtered at 4 to 5 and 65 weeks exhibited 13.6 and 95% of myopathies, respectively. Birds slaughtered at 65 weeks showed no occurrence of WB, isolated or combined with WS. It was not possible to differentiate the WB and WS/WB classes; therefore, those samples were grouped (WB+WS/WB). SPA-LDA model showed greater accuracy (92 to 93%) in identifying Normal (N), WS, and WB+WS/WB groups, compared to SIMCA (89 to 91%). It can be concluded that the level of occurrence of myopathies in meat is directly related to the age of slaughter. This study demonstrated that NIRS combined with SPA-LDA model could be used as a tool to detect myopathies in chicken breast. This technique has potential for application in industrial processing lines as an alternative to the traditional methods of identification. PRACTICAL APPLICATION: This study shows that NIRS combined with chemometric techniques can be used to identify chicken breast myopathies in a wide range of ages at slaughter. In addition to being able to discriminate chicken muscles into subclasses, namely, Normal, WS, and WB/WB+WS, this technique has potential for application in industrial processing lines as it is a portable and nondestructive method. This procedure is emphasized as an alternative to the conventional method of identification based on palpation and visual assessment of muscle.

HPLC-Based Chemometric Analysis for Coffee Adulteration

Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (-LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%-60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and -LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and -LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.

Environment-Friendly Nanoporous Titanium Dioxide with Enhanced Photocatalytic Activity

Nanoporous TiO2 anatase was environment-friendly prepared using coffee husk extract (CHE) as bio-template instead of hazardous chemicals and solvents and ultrasonic waves. Caffeine and caffeic acid were found to be the main compounds in CHE to modify the morphology of TiO2. The properties of as-prepared titanium dioxide particles were determined by different characterization techniques. The results demonstrate the formation of a meso/macro-porous channel consisting of small TiO2 particles (8–10 nm). The as prepared green nanoparticles exhibited improved photocatalytic activity for the degradation of organic water pollutants with good recyclability. The enhancement in efficiency of green nanoporous TiO2 can be attributed to higher surface area and the presence of more active adsorption sites inside the pores. The current research provides for a low cost, safe, and eco-friendly way to produce efficient photocatalysts for remediation of polluted water.

The Use of Partial Least Square Regression and Spectral Data in UV-Visible Region for Quantification of Adulteration in Indonesian Palm Civet Coffee

Asian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world's priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a robust and simple method for determining the adulteration of luwak coffee. In this research, the use of UV-Visible spectra combined with PLSR was evaluated to establish rapid and simple methods for quantification of adulteration in luwak-arabica coffee blend. Several preprocessing methods were tested and the results show that most of the preprocessing spectra were effective in improving the quality of calibration models with the best PLS calibration model selected for Savitzky-Golay smoothing spectra which had the lowest RMSECV (0.039) and highest RPD_cal value (4.64). Using this PLS model, a prediction for quantification of luwak content was calculated and resulted in satisfactory prediction performance with high both RPD _p and RER values.

Detection of Corn Adulteration in Brazilian Coffee (Coffea arabica) by Tocopherol Profiling and Near-Infrared (NIR) Spectroscopy

Coffee is a high-value commodity that is a target for adulteration, leading to loss of quality and causing significant loss to consumers. Therefore, there is significant interest in developing methods for detecting coffee adulteration and improving the sensitivity and accuracy of these methods. Corn and other lower value crops are potential adulterants, along with sticks and coffee husks. Fourteen pure Brazilian roasted, ground coffee bean samples were adulterated with 1-20% of roasted, ground corn and were analyzed for their tocopherol content and profile by HPLC. They were also analyzed by near-infrared (NIR) spectroscopy. Both proposed methods of detection of corn adulteration displayed a sensitivity of around 5%, thus representing simple and fast analytical methods for detecting adulteration at likely levels of contamination. Further studies should be conducted to verify the results with a much larger sample size and additional types of adulterants.