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CAMILLA MENOZZI

Dottorando
Dipartimento di Scienze della Vita


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Pubblicazioni

2024 - Addressing adulteration challenges of dried oregano leaves by NIR HyperSpectral Imaging [Articolo su rivista]
Ferrari, Veronica; Calvini, Rosalba; Menozzi, Camilla; Ulrici, Alessandro; Bragolusi, Marco; Piro, Roberto; Tata, Alessandra; Suman, Michele; Foca, Giorgia
abstract


2024 - Comparison of Different Spectral Ranges to Monitor Alcoholic and Acetic Fermentation of Red Grape Must Using FT‑NIR Spectroscopy and PLS Regression [Articolo su rivista]
Menozzi, Camilla; Foca, Giorgia; Calvini, Rosalba; Catellani, Lisa; Bezzecchi, Andrea; Ulrici, Alessandro
abstract

Wine vinegar is produced through a two-phase fermentation of grape must: initially, yeast converts grape sugars into ethanol, and subsequently, acetobacteria oxidize ethanol into acetic acid. This process, spanning weeks when conducted by surface fermentation, requires constant monitoring of ethanol and total acidity levels. To enhance the quality and efciency of process monitoring, vinegar production is shifting to faster, environmentally sustainable methods. Near-infrared (NIR) spectroscopy, recognized for its non-invasiveness and speed, is ideal for online implementation in process control. This study tracked dual fermentation in red grape must over an extended period, monitoring two diferent batches simultaneously to assess fermentation kinetics and reproducibility. Ethanol content and total acidity were analyzed in fermenting musts throughout the whole fermentation process using both classical laboratory analyses and FT-NIR spectroscopy. Principal Component Analysis (PCA) was used to explore the spectral dataset, then Partial Least Squares (PLS) was used to develop calibration models for predicting ethanol and acidity. The models calculated considering the entire spectral range were compared with those obtained for two narrower zones, where more cost-efective and easily miniaturizable sensors are available on the market. FT-NIR allowed to efectively determine ethanol content and acidity (R2 Pred>0.98), both over the entire range (12,500–4000 cm−1, corresponding to 800–2500 nm) and in the 10,526–6060 cm−1 (950–1650 nm) region. Although less satisfactory, still acceptable results were obtained in the 12,500–9346 cm−1 (800–1070 nm) region (R2 Pred>0.81), confrming the potential for cost-efective devices in real-time fermentation monitoring.


2023 - Design and application of a smartphone-based device for in vineyard determination of anthocyanins content in red grapes [Articolo su rivista]
Menozzi, C; Calvini, R; Nigro, G; Tessarin, P; Bossio, D; Calderisi, M; Ferrari, V; Foca, G; Ulrici, A
abstract

The choice of the proper moment for harvesting is a crucial aspect in winemaking process, since the chemical attributes of grape berries strongly influence red wine quality. In particular, phenolic composition of red grapes plays a significant role in many sensory properties of wine related to color and taste. Anthocyanins are the most important phenolic compounds for red grapes: they accumulate in the grape skin during ripening, and they are responsible for the purple color of ripe berries. Routine analysis for the determination of grapes phenolic maturity includes chromatographic and spectroscopic techniques, that are time-consuming and expensive. In this work, we propose an innovative device conceived for the determination of grape phenolic maturity based on RGB images of grape berries acquired with a smartphone. The device has been designed to be used directly in the vineyard thanks to its small size and to the possibility of acquiring geolocated images of the berries under controlled lighting conditions. In this study, grape samples of three different varieties (Ancellotta, Lambrusco Salamino and Sangiovese) were collected at different harvest times from veraison to maturity and imaged by means of a common smartphone using the device. The RGB images were then converted into one-dimensional signals, named colourgrams, which codify the color properties of the images. The dataset of colourgrams was then used to calculate calibration models using Partial Least Squares (PLS) regression in order to relate color information with chemical parameters generally employed to evaluate grape phenolic maturity, such as total anthocyanins content and extractable anthocyanins content. The calibration models were implemented in a software interface that allows to acquire geolocated images of the grape samples, visualize the outcomes of the analysis, visualize maps and plots related to phenolic maturity, store data and share relevant information.


2023 - Evaluation of the potential of near infrared hyperspectral imaging for monitoring the invasive brown marmorated stink bug [Articolo su rivista]
Ferrari, V.; Calvini, R.; Boom, B.; Menozzi, C.; Rangarajan, A. K.; Maistrello, L.; Offermans, P.; Ulrici, A.
abstract

The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops, compromising agri-food production. Field monitoring procedures are fundamental to perform risk assessment operations, in order to promptly face crop infestations and avoid economical losses. To improve pest management, spectral cameras mounted on Unmanned Aerial Vehicles (UAVs) and other Internet of Things (IoT) devices, such as smart traps or unmanned ground vehicles, could be used as an innovative technology allowing fast, efficient and real-time monitoring of insect infestations. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens on different vegetal backgrounds, overcoming the problem of BMSB mimicry. Hyperspectral images of BMSB were acquired in the 980–1660 nm range, considering different vegetal backgrounds selected to mimic a real field application scene. Classification models were obtained following two different chemometric approaches. The first approach was focused on modelling spectral information and selecting relevant spectral regions for discrimination by means of sparse-based variable selection coupled with Soft Partial Least Squares Discriminant Analysis (s-Soft PLS-DA) classification algorithm. The second approach was based on modelling spatial and spectral features contained in the hyperspectral images using Convolutional Neural Networks (CNN). Finally, to further improve BMSB detection ability, the two strategies were merged, considering only the spectral regions selected by s-Soft PLS-DA for CNN modelling.