Green and fast determination of the alcoholic content of wines using thermal infrared enthalpimetry.

An innovative use of thermal infrared enthalpimetry (TIE) is proposed for the determination of alcoholic content of red and white wines. Notwithstanding the presence of ethanol in beverages, absolute ethanol was added directly to wines, and the temperature rise caused by the heat of dilution was monitored using an infrared camera. Analytical signals were obtained in only 10 s for four samples simultaneously, and a calibration curve was constructed with hydroalcoholic reference solutions. A linear calibration curve was obtained from 3.0 to 18.0% (v/v) ethanol (R2 = 0.9987). The results showed agreement ranging from 98.2 to 104.0% with 942.06 and 969.12 methods of AOAC. Organic compounds (e.g., sugar) did not interfere in the determinations. The proposed method provided fast results, with a throughput of 480 samples per hour and negligible energy consumption (0.001 kWh). In addition, the consumption of reagents was reduced when compared with conventional method fulfilling green analytical chemistry requirements.

Infrared spectroscopy: a potential tool in huanglongbing and citrus variegated chlorosis diagnosis.

Huanglongbing (HLB) and citrus variegated chlorosis (CVC) are serious threats to citrus production and have caused considerable economic losses worldwide, especially in Brazil, which is one of the biggest citrus producers in the world. Neither disease has a cure nor an efficient means of control. They are also generally confused with each other in the field since they share similar initial symptoms, e.g., yellowing blotchy leaves. The most efficient tool for detecting these diseases is by polymerase chain reaction (PCR). However, PCR is expensive, is not high throughput, and is subject to cross reaction and contamination. In this report, a diagnostic method is proposed for detecting HLB and CVC diseases in leaves of sweet orange trees using attenuated total reflectance Fourier transform infrared spectroscopy and the induced classifier via partial least-squares regression. Four different leaf types were considered: healthy, CVC-symptomatic, HLB-symptomatic, and HLB-asymptomatic. The results show a success rate of 93.8% in correctly identifying these different leaf types. In order to understand which compounds are responsible for the spectral differences between the leaf types, samples of carbohydrates starch, sucrose, and glucose, flavonoids hesperidin and naringin, and coumarin umbelliferone were also analyzed. The concentration of these compounds in leaves may vary due to biotic stresses.