Physics

Process analytical technology

Partial least squares regression

Green coffee

Coffee grounds

Mathematics

Biology

Analytical Chemistry (journal)

Optics

Statistics

Matrix (chemical analysis)

Moisture

Paleontology

Composite material

Principal component analysis

Chemistry

Materials science

Coffee bean

Near-infrared spectroscopy

Food science

Environmental science

Chromatography

Bioprocess

Journal of Near Infrared Spectroscopy

The aim of this work was to compare two near infrared spectrometers (960–1650 nm) to assess coffee matrices for real-time characterization during processing. A benchtop spectrophotometer built for process measurement, equipped with a rotating acquisition system suitable for in-line analyses (device 1), and a portable spectrophotometer for at-line analyses (device 2) were tested. The experimentation was conducted on green, roasted bean, and ground coffee, relating to three coffee blends, employing samples collected at different steps from the process. A total of 399 (247 green, 76 roasted, and 76 ground coffee samples) and 229 (142 green, 43 roasted and 44 ground coffee samples) samples were analyzed using device 1 and device 2, respectively. Principal component analysis was applied to the spectra of the coffee samples to evaluate the feasibility of real-time characterization for each matrix type, during the different steps of the process. Partial least square regression analysis was applied to develop calibrations to predict moisture content, tap density, and powder granulometry on ground coffee and to estimate moisture on roasted bean with a view of real-time measurements. The Passing and Bablok regression method and Bland–Altman analysis were performed to compare results obtained from the tested devices. Considering the models built on different datasets were based on different blends, quality parameters, and matrices, the results obtained from partial least square models, in cross-validation, gave R 2 values between 0.21 and 0.98 and between 0.18 and 0.84 for devices 1 and 2, respectively. However, considering the comparison of model results derived from the two devices, no significant differences are noticeable for the most part of the dataset analyzed. Hence, based on the strategy to be undertaken by coffee industry operators, a future real-scale application could be envisaged directly in-line using device 1 or at-line using device 2.

Characterization of green, roasted beans, and ground coffee using near infrared spectroscopy: A comparison of two devices