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. 2024 Jul 18;13(14):2273.
doi: 10.3390/foods13142273.

Light Flux Density and Photoperiod Affect Growth and Secondary Metabolism in Fully Expanded Basil Plants

Luigi d'Aquino  1 , Rosaria Cozzolino  2 , Livia Malorni  2 , Thierry Bodhuin  3 , Emilia Gambale  1 , Maria Sighicelli  4 , Brigida Della Mura  5 , Cristina Matarazzo  2 , Sonia Piacente  6 , Paola Montoro  6
Affiliations

Light Flux Density and Photoperiod Affect Growth and Secondary Metabolism in Fully Expanded Basil Plants

Luigi d'Aquino et al. Foods. .

Abstract

Indoor production of basil (Ocimum basilicum L.) is influenced by light spectrum, photosynthetic photon flux density (PPFD), and the photoperiod. To investigate the effects of different lighting on growth, chlorophyll content, and secondary metabolism, basil plants were grown from seedlings to fully expanded plants in microcosm devices under different light conditions: (a) white light at 250 and 380 μmol·m-2·s-1 under 16/8 h light/dark and (b) white light at 380 μmol·m-2·s-1 under 16/8 and 24/0 h light/dark. A higher yield was recorded under 380 μmol·m-2·s-1 compared to 250 μmol·m-2·s-1 (fresh and dry biomasses 260.6 ± 11.3 g vs. 144.9 ± 14.6 g and 34.1 ± 2.6 g vs. 13.2 ± 1.4 g, respectively), but not under longer photoperiods. No differences in plant height and chlorophyll content index were recorded, regardless of the PPFD level and photoperiod length. Almost the same volatile organic compounds (VOCs) were detected under the different lighting treatments, belonging to terpenes, aldehydes, alcohols, esters, and ketones. Linalool, eucalyptol, and eugenol were the main VOCs regardless of the lighting conditions. The multivariate data analysis showed a sharp separation of non-volatile metabolites in apical and middle leaves, but this was not related to different PPFD levels. Higher levels of sesquiterpenes and monoterpenes were detected in plants grown under 250 μmol·m-2·s-1 and 380 μmol·m-2·s-1, respectively. A low separation of non-volatile metabolites based on the photoperiod length and VOC overexpression under longer photoperiods were also highlighted.

Keywords: LED lighting; indoor farming; plant metabolomics; precision agriculture; volatile organic compounds.

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Conflict of interest statement

Author Thierry Bodhuin was employed by the company FOS S.p.A., Via E. Melen 77, 16152 Genova, Italy. He participated in software and data curation in the study. The role of the company was as a co-owner of the Microcosm patent along with ENEA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The light spectrum in the wavelength region λ 380 ÷ 780 nm was determined at about 80 cm from the light sources.
Figure 2
Figure 2
Basil plants at the end of the cultivation period in the microcosms under white light at 250 (A) and 380 (B) μmol·m−2·s−1.
Figure 3
Figure 3
Average fresh (f, upper) and dry (d, lower) weights of leaves (L), stems (S), and inflorescence axes (F), and average weights of the total aerial biomasses (T) of plants in Microcosms 250 (‘250’) and 380 (‘380’) determined at harvest time. Bars indicate the mean values and standard errors (n = 6 replicates). Non-significant and significant differences at p ≤ 0.05 are indicated as ns and *.
Figure 4
Figure 4
Average fresh (f, upper) and dry (d, lower) weights of leaves (L), stems (S), and flowers (F), and average weights of the total aerial biomasses (T) of plants in Microcosms 16 (‘16’) and 24 (‘24’) determined at harvest time. Bars indicate the mean values and the standard errors (n = 6 replicates). Non-significant differences are indicated as ns.
Figure 4
Figure 4
Average fresh (f, upper) and dry (d, lower) weights of leaves (L), stems (S), and flowers (F), and average weights of the total aerial biomasses (T) of plants in Microcosms 16 (‘16’) and 24 (‘24’) determined at harvest time. Bars indicate the mean values and the standard errors (n = 6 replicates). Non-significant differences are indicated as ns.
Figure 5
Figure 5
PCA from the LC-ESI-LTQ-Orbitrap-MS pseudo-targeted analysis of non-volatile compounds in leaves from plants in the PPFD experiment. (A) The score scatter plot is colored to differentiate between 250 μmol·m−2·s−1 (250) and 380 μmol·m−2·s−1 (380). (B) The score scatter plot is colored to distinguish apical (A) leaves and middle (M) leaves. (C) Loading scatter plot.
Figure 6
Figure 6
PCA was performed on the GC-MS analysis of volatile compounds in samples from the PPFD experiment and PCA was obtained through data fusion of LC-MS and GC-MS data. (A) The volatile compound score scatter plot is colored to differentiate between 250 μmol·m−2·s−1 (250) and 380 μmol·m−2·s−1 (380). (B) Volatile compound loading scatter plot. (C) Data fusion score scatter plot. (D) Data fusion loading scatter plot.
Figure 7
Figure 7
PCA from the LC-ESI-LTQ-Orbitrap-MS pseudo-targeted analysis of non-volatile compounds in samples from the photoperiod experiment; 16, 16/8 h light/dark; 24, 24/0 h light/dark. (A) The score scatter plot for non-volatile metabolites. (B) The loading scatter plot for non-volatile metabolites. (C) Volatile metabolite score scatter plot. (D) Volatile metabolite loading scatter plot.
Figure 8
Figure 8
PCA was performed on a data fusion block, combining the results of LC-MS and GC-MS for the analysis of non-volatile and volatile compounds in samples from the photoperiod experiment; 16, 16/8 h light/dark; 24, 24/0 h light/dark. (A) Score scatter plot. (B) Loading scatter plot.
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